Mining Patterns in the Presence of Domain Knowledge

2005

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

International Journal of Computer Applications, 2018

Pattern mining is an important field of data mining. The fundamental task of data mining is to explore the database to find out sequential, frequent patterns. In recent years, data mining has shifted its focus to design methods for discovering patterns with user expectations. In this regard various types of pattern mining methods have been proposed. Frequent pattern mining, sequential pattern mining, temporal pattern mining, and constraint based pattern mining. Pattern mining has various useful real-life applications such as market basket analysis, e-learning, social network analysis, web page, click sequences, Bioinformatics, etc., this paper presents a survey of various types of pattern mining. The main goal of this paper is to present both an introduction to all pattern mining and a survey of various algorithms, challenges and research opportunities. This paper not only discusses the problems of pattern mining and its related applications, but also the extensions and possible future improvements in this field.

The extraction of patterns of interest and associations between them have been a major research topic since its definition at the beginning of the nineties. Abundant research studies have been dedicated to this field, providing overwhelming progresses in both efficiency and scalability, and extracting patterns from different data structures and domains. Since pattern mining is the keystone of data analysis, many application fields and, specially, numerous researchers have focused their attention on the discovery of patterns and associations that describe and represent any type of homogeneity and regularity in data. The growing scope of applications of pattern mining has deep impact on pattern mining models based on data domains, data dimensionality, data comprehensibility and data flexibility. All of this provides new and challenging research issues that need to be solved, broaden new research lines and leaving early pattern mining problems that can be considered as solved already.

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Knowledge and Information Systems, 2015

Twenty years of research on frequent itemset mining, or pattern mining, has led to the existence of a set of efficient algorithms for identifying different types of patterns, from transactional to sequential. Despite the great advances in this field, big data brought a completely new context to operate, with new challenges arising from the growth in data size, dynamics and complexity. These challenges include the shift not only from static to dynamic data, but also from tabular to complex data sources, such as social networks (expressed as graphs) and data warehouses (expressed as multi-relational models). In this new context, and more than ever, users need effective ways to control the large number of discovered patterns, and to be able to choose what patterns to consider at each time. The most accepted and common approach to minimize these drawbacks has been to capture and represent the semantics of the domain through constraints, and use them not only to reduce the number of results, but also to focus the algorithms in areas where it is more likely to gain information and return more interesting results. The use of constraints in pattern mining has been widely studied, and there are a lot of proposed types of constraints and pushing strategies. In this paper, we present a new global view of the work done on the incorporation of constraints in the pattern mining process. In particular, we propose a new framework for constrained pattern mining, that allows us to organize and analyze existing algorithms and strategies, based on the different types and properties of constraints, and on the data sources they are able to handle. Keywords Data mining • Pattern mining • Domain knowledge • Constraints This work was partially supported by FCT-Fundação para a Ciência e a Tecnologia, under Project D2PM

Data mining is viewed as an essential part of the process towards knowledge discovery. Through data mining process different kinds of patterns that is frequent pattern and others, are discovered, evaluated and presented as knowledge. Mining data from large database repositories which contains vast amount of data is not only interesting but also essential since it yields useful and novel information which aid organizations and other individuals in decision making. Many previous research works based on frequent pattern mining algorithm and association rule mining are focused on transactional data, yet there are other interesting types of data which requires just about same study in order to perform mining techniques on them so as to discover novel information. This paper elaborates mining of Object-relational data in relation to transactional data as a base of understanding, later uses differed mining algorithms to uncover frequent patterns and evaluate performances of these algorithms. Two approaches for this mining task was proposed, namely fundamental approach and nested-relations approach.

Emerging Research in Computing, Information, Communication and Applications, 2015

In this paper, an algorithm is proposed for mining frequent maximal itemsets. Discovering frequent itemsets is the key process in association rule mining. One of the major drawbacks of traditional algorithms is that they work only for items with single frequency per transaction. Proposed algorithm works with multiple frequency of an item per transaction. Proposed algorithm scans base database only once. The proposed algorithm took lesser time to find candidate itemsets. It uses directed graph for candidate itemsets generation. We conducted experiments on two datasets, Mushroom and Chess. Experimental results showed that proposed algorithm can quickly discover maximal frequent itemsets and effectively mine potential association rules.

Mining association rule is one of the key problems in data mining approach. Association rules discover the hidden relationships between various data items. In this paper, we propose a framework for the discovery of association rules using frequent pattern mining. We use preprocessing to transform the transaction dataset into a 2D matrix of 1's and 0's. Mining association rule must firstly discover frequent itemsets and then generate strong association rules from the frequent itemsets. The Apriori algorithm is the most well known association rule mining algorithm and is less efficient because they need to scan the database many times and store transaction ID in memory, so time and space overhead is very high. Especially they are less efficient when they process large scale database. Here we propose improved Apriori algorithm by including prune step and hash map data structure. The improved algorithm is more suitable for large scale database. Experimental results shows that computation times are reduced by using the prune step and hash map data structure.

International Journal of Computer Applications

One of the major concerns in the field of knowledge discovery is the interestingness problem and the unreasonable number of association rules being mined. The past studies confirm that although a large number of rules are mined for each query, they do not seem to satisfy user’s expectations. The methods already proposed in the literature like post-processing and algorithms to reduce itemsets and nonredundant rules do not always guarantee mining of interesting rules for the user. In conventional Data Mining, the usefulness of association rules is limited by the huge amount of delivered rules. In this paper we propose a new interactive approach ‘Onto-Mine’ to trim and filter the discovered rules. We propose to integrate user knowledge in association rule mining by combining Domain Ontology and interactive intelligence. First, we use Domain and Background Ontology with user knowledge and this interactive intelligence of Onto-Mine assists the user throughout the analyzing task and helps...

WIREs Data Mining and Knowledge Discovery, 2017

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently co-occurring in databases. Due to its numerous applications in domains such as bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis, itemset mining has become a popular research area. This paper provides an up-to-date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high-utility itemset mining, rare itemset mining, fuzzy itemset mining and uncertain itemset mining. The paper also discusses research opportunities and the relationship to other popular pattern mining problems such as sequential pattern mining, episode mining, sub-graph mining and association rule