Association Mining

In the present paper a model of a multi agent based system is presented, which helps marketers on the one hand to address its products to the best targets and in the another hand to generate relevant product recommendations for customers that best match their interests and needs. To achieve this, the system is based on six agents. Each one of them has a specified role but can also communicate with other agents to share knowledge and achieve common goals. In order to generate relevant recommendations and target the best customers, the system uses different types of parameters (the customer‟s parameters, product‟s parameters, parameters of the context and the constraints). General Terms Product recommendation, customer selection.

In the last years, the problem of Frequent Itemset Mining (FIM) from imperfect databases has been sufficiently tackled to handle many kinds of data imperfection. However, frequent itemsets discovered from databases describe only the current state of the data. In other words, when data are updated, the frequent itemsets could no longer reflect the data, i.e., the data updates could invalidate some frequent itemsets and vice versa, some infrequent ones could become valid. In this paper, we try to resolve the problem of Incremental Maintenance of Frequent Itemsets (IMFI) in the context of evidential data. We introduce a new maintenance method whose experimentations show efficiency compared to classic methods.

leverage, two traditional frequency-based measures. Among the top 50 signal pairs (i.e., enalapril versus symptoms) ranked by the potential causal-leverage measure, the physicians on the project determined that eight of them probably represent true causal associations.

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Different data mining algorithms applied to the same data can result in similar findings, typically in the form of rules. These similarities can be exploited to identify especially powerful rules, in particular those that are common to the different algorithms. This research focuses on the independent application of association and classification mining algorithms to the same data to discover common or similar rules, which are deemed ''persistent-rules". The persistent-rule discovery process is demonstrated and tested against two data sets drawn from the American National Election Studies: one data set used to predict voter turnout and the second used to predict vote choice.

We propose DepMiner, a method implementing a simple but effective model for the evaluation of itemsets, and in general for the evaluation of the dependencies between the values assumed by a set of variables on a domain of finite values. This method is based on ∆, the departure of the probability of an observed event from a referential probability of the same event. The observed probability is the probability that the variables assume in the database given values; the referential probability, is the probability of the same event estimated in the condition of maximum entropy. DepMiner is able to distinguish between dependencies among the variables intrinsic to the itemset and dependencies "inherited" from the subsets: thus it is suitable to evaluate the utility of an itemset w.r.t. its subsets. The method is powerful: at the same time it detects significant positive dependencies as well as negative ones suitable to identify rare itemsets. Since ∆ is anti-monotonic it can be embedded efficiently in algorithms. The system returns itemsets ranked by ∆ and presents the histogram of ∆ distribution. Parameters that govern the method, such as minimum support for itemsets and thresholds of ∆ are automatically determined by the system. The system uses the thresholds for ∆ to identify the statistically significant itemsets. Thus it succeeds to reduce the volume of results more then competitive methods.

Document management has to be rethinked and clarified in organizations, especially for the coordinated adoption of organization-wide electronic document management systems (EDMSs). This paper reports the identification and evaluation of 11 organizational document genres of an industrial organization by using a framework constituted in an earlier study. The analysis revealed that different features need to be developed for different document genres. The results indicate that universal definitions for a "document" should not be used to envision organizational EDMSs. Rather, organizational document genres and genre systems should be systematically rethinked in collaboration with information systems specialists, organization designers and domain experts.

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the inherent feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

The paper presents a survey of the field of recommender systems and describes current recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. The paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities. These extensions include, among others, improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multi-criteria ratings, and provision of more flexible and less intrusive types of recommendations.

With the ever-growing database sizes, we have enormous quantities of data, but unfortunately we cannot use raw data in our day-today reasoning/decisions. We desperately need knowledge. This knowledge is in most cases in the gathered data, but the extraction of it is a very time and resources consuming operation. Association rule mining is a central problem in discovering knowledge. It finds interesting association or correlation relationships among a large set of data items. The paper presents some considerations about distributed association rules mining together with a comparison between three representative distributed algorithms, namely CDA, FDM and DDM. The compared algorithms are presented together with some experimental data that leads to the final conclusions.

This paper is concerned with current applications and researches of GUHA, a method for hypothesis generation. The GUHA method is very promising in the field of association rules data mining. Some of the current software implementations of the GUHA procedures offer simple mechanism of integration with BI processes, especially where large data sets are being processed. To use data for processing, it must first be pre-processed or aggregated and after that, as a data set to be used by the implementations of the GUHA procedures. The most preferred processing data representation are bit string cards of categories, mainly because logical operations can be easily performed on such representation of data. Since GUHA has a significant statistical and logical background it is taken as very relevant in positive and negative association rules mining, and also in interesting hypothesis discovery.

Association rule mining is a crucial data mining technique used to uncover relationships between variables in large datasets. This paper provides a comprehensive review of various association rule algorithms, including Apriori, FP-Growth, ECLAT, AIS, and SETM. Each algorithm is discussed in terms of its methodology, advantages, and limitations. The paper also explores advanced extensions such as Multi-Level and Multi-Dimensional Association Rules, which offer deeper insights by incorporating hierarchical and multi-attribute dimensions into the analysis. By examining the evolution of these techniques, the paper highlights ongoing challenges, such as scalability, efficiency, and interpretability, and suggests future research directions, including the integration of association rule mining with other data mining techniques and the development of algorithms for complex data types. This review aims to provide a detailed understanding of the state-of-the-art in association rule mining and its practical applications.

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. Based on this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multirelational association rule mining where the relations exist between image-based features, and texual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images. In this paper we also present a multi-relational extension to the FP-Tree algorithm to accomplish the association rule mining task more effectively compared to the currently used decentralized version of the apriori algorithm. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our autoannotation experiments on different test sets.

In this paper, we propose an associative watermarking scheme which is conducted by the concept of Association Mining Rules (AMRs) and the ideas of Vector Quantization (VQ) and Soble operator. Performing associative watermarking rules to the images will reduct the amount of the embedded data, and using VQ indexing scheme can easily recall the embedded watermark for the purpose of image authentication, and establishing the relation between the association rules on both the original image and the watermark image. The Vector Quantization decoding technique is applied to reconstruct the watermarked image from the watermarked index table. The experimental result shows that the proposed scheme is robust. When the watermarked images suffered from various kinds of image-processing procedures, such as Gaussian noise, brightness, blurring, sharpening, cropping, and JPEG lossy compression can be detected without the original images assistance.

This paper proposes a systemic framework that attempts to define the domain and major areas of Data Mining and Knowledge Discovery (DMKD). Grounded theory approach, a qualitative method that inductively develops an understanding of phenomena, is adopted to build the framework. Using a large collection of DMKD literature, including DMKD journals, conference proceedings, syllabuses, and dissertations, this study develops a framework of eight main areas for the field: (1) foundations of DMKD, (2) learning methods & techniques, (3) mining complex data, (4) highperformance & distributed data mining, (5) data mining software & systems, (6) data mining process & project, (7) data mining applications, (8) data mining tasks. The last area is suggested as the central theme of the field.

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. Based on this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multirelational association rule mining where the relations exist between image-based features, and texual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images. In this paper we also present a multi-relational extension to the FP-Tree algorithm to accomplish the association rule mining task more effectively compared to the currently used decentralized version of the apriori algorithm. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our autoannotation experiments on different test sets.

In the recent past, wide ranges of video retrieval processes were presented by different researchers. In order to boost the ease of access of video clip, keen applications, which have item removal, video purchasing, video clip healing and also fast perusing are performed with foundation demonstrating and also recovery summation. That is, both vital outline video reading and also explicit product recuperation reading can be performed on the very first video clip. Secondly, the techniques based on material matching for video access are evaluated. In view of these actualities, we provide a query by the criterion video clip recovery method, in this paper. We present a computation for comparability collaborating, to find the neighbourhood video clip setups with various sizes. In addition, the computation can find the similitude between an inquiry video clip and an item of another video clip group. Amid this treatment, a certainty price will be taken into consideration that makes the inexact similitude coordinating possible. The access methods based on info concept are additionally reviewed. In the recent years, a substantial arrangement of video documents is made and quickly sight, and also sound growth makes a brand-new examination aware preparing the world. Our proposed technique is in view of detailed theory. These structures consist of three essential components which integrate: shot border detection, ordered video clip synopsis, and submit and recover target video clip.

In recent years interest has grown in "mining" large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an emerging research area. Association rules discovery is an important KDD technique for better data understanding. This paper proposes an enhancement with a memory efficient data structure of a quantitative approach to mine association rules from data. The best features of the three algorithms (the Quantitative Approach, DHP, and Apriori) were combined to constitute our proposed approach. The obtained results accurately reflected knowledge hidden in the datasets under examination. Scale-up experiments indicated that the proposed algorithm scales linearly as the size of the dataset increases.

Association rules consist of the discovery of association between mining transaction items. This is one of the most important information mining jobs. It has been integrated into many commercial data mining software and has a wide variety of applications on a number of domains. So, computing the prediction rules in top rank data set is very difficult task. Finding the pattern in large data set require memory computational power high rate of I/O. and it is possible only on high computational machine. In this paper, selection of parameter which is used to compute is chosen based on minimum support and minimum confidence value. In this paper proposed a new algorithm which generates the association rule for the input parameters to finding the pattern in large data set. The algorithm starts searching the rules. As soon as a rule is found, it is added to the list of order rules list by support. The list is used so far to maintain top N rules found. Once valid rules are found, the minimum ...

Pattern mining has emerged as a compelling field of data mining over the years. Literature has bestowed ample endeavors in this field of research ranging from frequent pattern mining to rare pattern mining. A precise and impartial analysis of the existing pattern mining techniques has therefore become essential to widen the scope of data analysis using the notion of pattern mining. This paper is therefore an attempt to provide a comparative scrutiny of the fundamental algorithms in the field of pattern mining through performance analysis based on several decisive parameters. The paper provides a structural classification of the widely referenced techniques in four pattern mining categories: frequent, maximal frequent, closed frequent and rare. It provides an analytical comparison of these techniques based on computational time and memory consumption using benchmark real and synthetic data sets. The results illustrate that tree based approaches perform exceptionally well over level wise approaches in case of dense data sets for all the categories. However, for sparse data sets, level wise approaches performed better than the former ones. This study has been carried out with an aim to analyze the pros and cons of the well known pattern mining techniques under different categories. Through this empirical study, an endeavor has been made to enable the researchers identify some fruitful and promising research directions in one of the most remarkable area of research, pattern mining.

Data mining has been defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining and sequential mining analysis are considered as crucial components of strategic control over a broad variety of disciplines in business, science and engineering. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered. In Sequence mining, data are represented as sequences of events, where order of those events is important. Finding patterns in sequences is valuable for predicting future events. In many applications such as the WEB applications, stock market, and genetic analysis, finding patterns in a sequence of elements or events, helps in predicting what could be the next event or element. At the conceptual level, association mining and sequence mining are two similar processes but using different representations of data. In association mining, items are distinct and the order of items in a transaction is not important. While in sequential pattern mining, the order of elements (events) in transactions (sequences) is important, and the same event may occur more than once. In this paper, we propose a new mapping function that maps event sequences into itemsets. Based on the unified representation of the association mining and the sequential pattern, a new approach that uses the Boolean representation of input database D to build a Boolean matrix M. Boolean algebra operations are applied on M to generate all frequent itemsets. Finally, frequent items or frequent sequential patterns are represented by logical expressions that could be minimized by using a suitable logical function minimization technique.

Compared with traditional association rule mining in the structured world (e.g. Relational Databases), mining from XML data is confronted with more challenges due to the inherent flexibilities of XML in both structure and semantics. The major challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger size for each data element. In order to make XML-enabled association rule mining truly practical and computationally tractable, we propose a practical model for mining association rules from XML documents and demonstrate the usability and effectiveness of model through a set of experiments on real-life data.

One of the popular and compact trie data structure to represent frequent patterns is via frequent pattern tree (FP-Tree). There are two scanning processes involved in the original database before the FP-Tree can be constructed. One of them is to determine the items support (items and their support) that fulfill minimum support threshold by scanning the entire database. However, if the changes are suddenly occurred in the database, this process must be repeated all over again. In this paper, we introduce a technique called Fast Determination of Item Support Technique (F-DIST) to capture the items support from our proposed Disorder Support Trie Itemset (DOSTrieIT) data structure. Experiments through three UCI benchmark datasets show that the computational time to capture the items support using F-DIST from DOSTrieIT is significantly outperformed the classical FP-Tree technique about 3 orders of magnitude, thus verify its scalability.

Data-mining is the extraction of meaningful patterns from the large source of data. Association Rule Mining (ARM) is an important data mining technique. Mining of frequent patterns is a very important association rule mining problem. The previous approach i.e. Apriori suffers from the candidate-generation and test mechanism. The Apriori approach becomes inefficient when either the length of the frequent set or length of the Transaction Database (TDB) increases. The algorithm adopts bottom up breadth first approach for the mining purpose. In this research work, we have proposed a Reduced Scanning Transaction Database (RSTDB) algorithm that uses certain heuristic function which reduces the number of Transaction Database passes required to generate the maximum frequent set required for Association Rule Mining (ARM).The approach is a hybrid of bottom up and top down approach. It uses both upward and downward closure properties for frequent item sets evaluation. In this work we will compare the Apriori approach with the above proposed approach for frequent pattern mining. We will try to evaluate the shortcoming of the proposed approach and also look as to how much efficient it is and in which cases. The RSTDB algorithm not only reduces the database scans but also will help in reducing the number of candidategeneration for a phase that is having a value less than the minimum support threshold value.

One of the main issues in the process of Knowledge Discovery in Databases is the Mining of Association Rules. Although a great variety of pattern mining algorithms have been designed to this purpose, their main problems rely on in the large number of extracted rules, that need to be filtered in a post-processing step resulting in fewer but more interesting results. In this paper we suggest a new algorithm, that allows the user to explore the rules space locally and incrementally. The user interests and preferences are represented by means of the new proposed formalism-the Rule Schemas. The method has been successfully tested on the database provided by Nantes Habitat.

One of the main challenges in data-intensive sectors like scientific research, data mining, and machine learning is efficiently analyzing enormous datasets. A popular data structure in similarity search algorithms to speed up the retrieval of closest neighbors is the N-List. In this paper, a high-performance method for mining frequent item sets called EN-list is presented. It represents item sets using an N-list and finds frequently recurring item sets directly using an aset-enumeration search tree. Specifically, it drastically reduces the search field by applying the powerful pruning approach known as Children-Parent Equivalency pruning. We conducted extensive experiments to compare En-list against three state-of-the-art algorithms: Fin, PrePost, and DiffNodesets on four distinct real datasets. The experimental results show that EN-list is always the fastest approach across all datasets. Furthermore, EN-list shows good memory consumption performance, requiring less memory than DiffNodesets and PrePost methods and just slightly more than the Fin approach.

Mining generalized association rules among items in the presence of taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum supports to be uniformly specified for all items or items within the same taxonomy level. This constraint would restrain an expert from discovering more interesting but much less supported association rules. In our previous work, we have addressed this problem and proposed two algorithms, MMS_Cumulate and MMS_Stratify. In this paper, we examined the problem of maintaining the discovered multi-supported, generalized association rules when new transactions are added into the original database. We proposed two algorithms, UD_Cumulate and UD_Stratify, which can incrementally update the discovered generalized association rules with non-uniform support specification and are capable of 2 effectively reducing the number of candidate sets and database re-scanning. Empirical evaluation showed that UD_Cumulate and UD_Stratify are 2-6 times faster than running MMS_Cumulate or MMS_Stratify on the updated database afresh.

The development of novel platforms and techniques for emerging "Big Data" applications requires the availability of real-life datasets for data-driven experiments, which are however not accessible in most cases for various reasons, e.g., confidentiality, privacy or simply insufficient availability. An interesting solution to ensure high quality experimental findings is to synthesize datasets that reflect patterns of real ones. A promising approach is based on inverse mining techniques such as inverse frequent itemset mining (IFM), which consists of generating a transactional dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent and infrequent ones. This paper describes an extension of IFM that considers more structured schemes for the datasets to be generated, as required in emerging big data applications, e.g., social network analytics.

Different data mining algorithms applied to the same data can result in similar findings, typically in the form of rules. These similarities can be exploited to identify especially powerful rules, in particular those that are common to the different algorithms. This research focuses on the independent application of association and classification mining algorithms to the same data to discover common or similar rules, which are deemed ''persistent-rules". The persistent-rule discovery process is demonstrated and tested against two data sets drawn from the American National Election Studies: one data set used to predict voter turnout and the second used to predict vote choice.

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

Abstract: Stream analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Stream data is a sequence of observations collected over intervals of time. Each data stream describes a phenomenon. Analysis on Stream data includes discovering trends (or patterns) in a Stream sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially ...

Mining frequent subtree patterns has many useful applications in XML mining, bioinformatics, network routing, etc. Most of the frequent subtree mining algorithms (such as FREQT, TreeMiner and CMTreeMiner) use anti-monotone property in the phase of candidate subtree generation. However, none of these algorithms seems to verify the correctness of this property in all kinds of tree structured data. In this paper, we investigate the correctness of anti-monotone property for the problem of weighted frequent subtree mining and use multiple examples to elaborate on our finding about this issue. It is shown that anti-monotonicity does not generally hold, when using weighed support in tree pattern discovery. Consequently, the tree mining algorithms that are based on this property would miss some of the valid frequent subtree patterns if one considers weighted support. We propose also a novel algorithm named W3-Miner that uses new data structures and techniques for full extraction of frequent subtrees. The experimental results confirm that W3-Miner finds some frequent subtrees that the previously proposed algorithms would not discover when if weighted support is required.

Indirect association is a new kind of infrequent pattern, which provides a new way for interpreting the value of infrequent patterns and can effectively reduce the number of uninteresting infrequent patterns. The concept of indirect association is to "indirectly" connect two rarely co-occurred items via a frequent itemset called mediator, and if appropriately utilized it can help to identify real interesting "infrequent itempairs" from databases. Indirect association rule is said to be positive (Negative) if mediator set contains presence (presence or absence) of items. Existing indirect association mining methods mine positive mediator sets. To the best of our knowledge, no research work has been conducted on mining indirect negative associations. In this paper, we propose an approach for mining indirect negative associations. The proposed method can discover all positive and negative indirect association between itemsets.

We present a framework to analyse text streams with minute details of a game and generate summaries for multiple paradigms of desired lengths (as determined by the user). Multiple paradigms refer to a summary that is player-specific, team-specific, event-specific (such as all the goals, sixes, home-runs, etc., in a game, depending on the sport), etc. The summary generation is based on building a dictionary of words specific to the sport to be able to recognise events in the game. As a next step, inputs are solicited from the sports fan with regard to the paradigm they are interested in and length of the summary that interests them. Based on these inputs, a template for the desired summary is generated. A detailed commentary of the game is mined and filtered for the events in the game that are related to the query. These events are then used to instantiate the template with appropriate descriptors. A novel feature of the template generation and instantiation is that a framework of sentences and appropriate descriptors is created based on a stochastic process that selects from a viable pool of alternatives to ensure variety in the summary that is generated. The framework has been tested on commentary from cricket games, that can be a few hours long (Twenty20 or T20 format) to five days long (test matches). A qualitative assessment of the summaries through human ratings is found to be consistent, with scores being equal to or in excess of 3 on a scale of 1-5 for all the parameters tested.

A core issue of the association rule extracting process in the data mining field is to find the frequent patterns in the database of operational transactions. If these patterns discovered, the decision making process and determining strategies in organizations will be accomplished with greater precision. Frequent pattern is a pattern seen in a significant number of transactions. Due to the properties of these data models which are unlimited and high-speed production, these data could not be stored in memory and for this reason it is necessary to develop techniques that enable them to be processed online and find repetitive patterns. Several mining methods have been proposed in the literature which attempt to efficiently extract a complete or a closed set of different types of frequent patterns from a dataset. In this paper, a method underpinned upon Cellular Learning Automata (CLA) is presented for mining frequent itemsets. The proposed method is compared with Apriori, FP-Growth and BitTable methods and it is ultimately concluded that the frequent itemset mining could be achieved in less running time. The experiments are conducted on several experimental data sets with different amounts of minsup for all the algorithms as well as the presented method individually. Eventually the results prod to the effectiveness of the proposed method.

Association rule mining perhaps the most widely described technique among the minding paradigms. The temporal association rule mining in the association rule mining tries to find relations among items in datasets. The temporal association mining has strength in detecting the dynamic nature of databases. Unfortunately the current mining methods ignore the consideration of database content updates. In the current research we have introduced the Incremental Standing method for Segment Progressive Filter (ISPF). The proposed technique can support the database update and mine updated datasets. We prove that the proposed algorithm is an optimal way of mining. We have applied the scan reduction technique to generate all candidate k-item sets to form 2-candidate item sets directly. We also bring good experimentation to validate and document clearly the algorithm with good examples.

Organizations are more interested in the interesting data rather than the bulk of data. So they need a systematic and scientific approach to extract meaningful data out of heaps of the data and to find out the relations among these patterns. To analyze &quot;big data&quot; on clouds, it is very important to research data mining strategies based on cloud computing paradigm from both theoretical and practical views. In this paper, based on the original Apriori algorithm, an improved algorithm is proposed which adopts a new count-based method to prune candidate item sets and uses generation record to reduce total data scan amount and also make it more modeling oriented. Experiments demonstrate that by performing our algorithm of on given datasets we will find the Solution of problems in association and apriori algorithm.

Eclat is an algorithm that finds frequent itemsets. It uses a vertical database and calculates item&#39;s support by intersecting transactions. However, Eclat suffers from the exponential time complexity of calculating the intersection of transactions. In this paper, a randomized algorithm called BloomEclat based on Bloom filter is presented to improve the Eclat algorithm complexity in finding frequent itemsets. Through Bloom Filter, an element’s membership to a set, can be checked and set operations such as intersection and union of two sets can be executed in a time efficient manner. By using these capabilities, Eclat algorithm’s intersecting problem can significantly improve. In BloomEclat algorithm with slight false positive error, the speed of the intersecting transactions is increased, and consequently the execution time is reduced.

Frequent and infrequent itemset mining are trending in data mining techniques. The pattern of Association Rule (AR) generated will help decision maker or business policy maker to project for the next intended items across a wide variety of applications. While frequent itemsets are dealing with items that are most purchased or used, infrequent items are those items that are infrequently occur or also called rare items. The AR mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, patterns, association or casual structures among set of items in the transaction databases or other data repositories. The design of database structure in association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The efforts on horizontal format suffers in huge candidate generation and multiple database scans which resulting in higher memory consumptions. To overcome the issue, the solutions on vertical approaches are proposed. One of the established algorithms in vertical data format is Eclat.ECLAT or Equivalence Class Transformation algorithm is one example solution that lies in vertical database format. Because of its 'fast intersection', in this paper, we analyze the fundamental Eclat and Eclatvariants such asdiffsetand sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. In this paper, we present the performance of Postdiffset algorithm prior to implementation in mining of infrequent or rare itemset. Postdiffset algorithm outperforms 23% and 84% to diffset and sortdiffset in mushroom and 94% and 99% to diffset and sortdiffset in retail dataset.

In this paper, a robust optimization approach is used to solve the redundancy allocation problem (RAP) in series-parallel systems with component mixing where uncertainty exists in components' reliabilities. In real world, the reliabilities of components are imprecisely estimated or the reliability of some components may vary due to some realistic factors. Therefore, we may deal with a system where there are many components with uncertain values of reliabilities. To deal with this problem, for the first time a robust optimization approach is applied to RAP with component mixing to produce a robust solution, which is relatively insensitive with respect to uncertainty in reliability of components. In addition, the advantages of the proposed robust technique are illustrated by considering a series-parallel system and finding the suitable redundancy levels and then Monte Carlo simulation is implemented to examine the quality of the robust solutions. The results indicate that applying the proposed robust RAP can be more reliable to determine system reliability in the designing phase of systems.

Data mining has been defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered. The efforts of most researchers focus on discovering rules in the form of implications between itemsets, which are subsets of items that have adequate supports. Having itemsets as both antecedent and precedent parts was motivated by the original application pertaining to market baskets and they represent only the simplest form of predicates. This simplicity is also due in part to the lack of a theoretical framework that includes more expressive predicates. The framework we develop derives from the observation that information retrieval and association mining are two complementary processes on the same data records or transactions. In information retrieval, given a query, we need to find the subset of records that matches the query. In contrast, in data mining, we need to find the queries (rules) having adequate number of records that support them. In this paper we introduce the theory of association mining that is based on a model of retrieval known as the Boolean Retrieval Model. The potential implications of the proposed theory are presented.

Association Mining, a class of data mining techniques, is one of the most researched field in data mining, where algorithms are designed to discover rules that reflect dependencies among values of an attribute. Because of the vast amounts of data that businesses store, most association mining algorithms are computationally expensive, where many passes over data are performed. Besides working on the sequential processing environment, the implementation of data mining ideas should consider parallel computing environments. In this paper, a new technique is presented to perform association mining based on the matrix approach. The new technique can be applied on the sequential and parallel environments. In the proposed technique, the data records are only scanned once to construct a frequency vector and a binary association matrix. Two algorithms, one for generating only maximal large item-sets and the other for generating all large item-sets, are presented. The number of disk accesses, CPU time, and memory space needed for generating large item-sets are O(n), O(N 2) , and O(N), respectively, where n is the number of input transactions, and N is the number of transaction groups.

Abstract: Stream analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Stream data is a sequence of observations collected over intervals of time. Each data stream describes a phenomenon. Analysis on Stream data includes discovering trends (or patterns) in a Stream sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially ...

Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In our study, we emphasis on the use of data mining techniques on time series, where mining techniques and tools are used in an attempt to recognize, anticipate and learn the time series behavior with different directly related or looked unrelated factors. Targeted ...

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. Based on this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multirelational association rule mining where the relations exist between image-based features, and texual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images. In this paper we also present a multi-relational extension to the FP-Tree algorithm to accomplish the association rule mining task more effectively compared to the currently used decentralized version of the apriori algorithm. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our autoannotation experiments on different test sets.

Version control systems are among the type of repositories that are frequently explored as sources of software change history. They can be mined to identify associations between software module modifications. This information is useful to support software modification activities, indicating to software engineers which modules are usually modified together during software maintenance or evolution. Previous works published on the subject focused on mining associations from open source software projects. This article presents the use of association mining in an industrial environment. The study was set up as a formal experiment and studied 18 systems developed in a large Brazilian beverage company. The results show that the precision of the rules obtained in this environment are even higher than its counterpart obtained in open source projects. This suggests that this approach is very useful in this type of environment.

In this work we made a study of several other works were the association and sequence mining techniques were applied to the field of web usage mining. This report is to be submitted to classification to the Data Mining course at the phd program "Diseno, Analisis y Aplicaciones de Sistemas Inteligentes", of University of Granada.