wy lin

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Address: Kaohsiung, T'ai-wan, Taiwan

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CBW,,-A Fast Algorithm for Activating Remining of Association Rule

Nowadays, association rules mining has become one of the predominate tasks employed to discover i... more Nowadays, association rules mining has become one of the predominate tasks employed to discover informotive rules from large data set to support decision-moking. One of the major dflculties in applying associations mining technique is the setting of an appropriate minimum support. Unfortunately, A large support threshold would hinder the discovery of some rare but informative rules. In this paper, we propose a novel algorithm called CBW,,, By keeping and utilizing the set of frequent itemsets MF and on auxiliary set of infrequent a-itemsets MFm the proposed CBW,. algorithm can significantly reduce, over on order of magnitude, the computation time spent on re- discovery offrequent itemsets.

Selected Papers from TAAI2005

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2007

The annual Conference on Artificial Intelligence and Applications (TAAI), held in Taiwan and host... more The annual Conference on Artificial Intelligence and Applications (TAAI), held in Taiwan and hosted by the Taiwanese Association of Artificial Intelligence and other academic institution, has expanded to an international forum following the success of its previous nine conferences. It hopes to bring together researchers and scholars in all areas of artificial intelligence from leading institutes and industries worldwide to exchange ideas on these hi-tech fields. Over 200 papers and 6 special issues consisting of 30 invited papers were presented, making TAAI 2005 the greatest in the TAAI series. This special issue presents 11 papers from this prestigious event, each of which has received outstanding recommendations from the reviewers and has been revised by authors for this publication. These papers detail a variety of applications of different artificial intelligent technologies, ranging from nonlinear programming, data classification, dynamic knowledge acquisition, and complex time...

OLAP data cubes configuration with genetic algorithms

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and their Complex Interactions' (Cat. No.00CH37166)

We deploy a genetic algorithm to automate the selection of OLAP data cubes. In particular, we con... more We deploy a genetic algorithm to automate the selection of OLAP data cubes. In particular, we concentrate on constraint-handling techniques. We propose a greedy repair method to deal with the problem of infeasible candidates that violate constraints. As the experiments show, this repair method is effective in exploring as well as exploiting the search to find a good solution

Detecting the Data Group Most Prone to a Specific Disguise Value

Lecture Notes in Computer Science, 2014

Disguised missing data, an emerging data quality problem coined by Pearson in 2006, is a special ... more Disguised missing data, an emerging data quality problem coined by Pearson in 2006, is a special kind of missing data that refers to values not exactly missing in the data entries, but cannot reflect the fact and so may lead to severe bias on analysis results. In this paper, we present a novel problem of detecting disguised missing data, i.e., finding out the data group most prone to a specific disguise value. We show that this problem can be formalized as an optimization problem and so a genetic-algorithms-based method is proposed to handle this problem. According to preliminary experimental results conducted on real datasets, our method can discover the same optimal data groups obtained by exhaustive method. A further evaluation on the FDA adverse drug event reporting dataset shows that our method yields similar results concluded by manual examinations performed by experienced analyzers.

Ensemble ADR Detection from Spontaneous Reporting Data: A Positive Unlabeled Learning Perspective

This paper presents our endeavor to fuse positive unlabeled and ensemble learning to shape a bett... more This paper presents our endeavor to fuse positive unlabeled and ensemble learning to shape a better ADR signal detector from spontaneous reporting data, like FAERS. Clinical Relevance—The results show that the proposed PU-ensemble methods surpass the non-PU ensemble with 18%, 144%, 19%, and 80% improvement on accuracy, precision, recall, and F-measure, respectively, of the detected ADR signals.

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Privacy-Preserving SRS Data Anonymization by Incorporating Missing Values

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI), 2018

Spontaneous Reporting Systems (SRSs) refer to systems used to collect voluntary reporting of adve... more Spontaneous Reporting Systems (SRSs) refer to systems used to collect voluntary reporting of adverse drug events (ADEs), which usually contain sensitive personal privacy information. Although many scholars have proposed various privacy protection models, they overlooked characteristics of SRS data. We previously have proposed a feasible privacy model and anonymization method dedicate to SRS data. However, this method is only applicable to complete data, not considering the fact that SRS data contain a lot of missing data. In this paper, we propose a new privacy model Closed MS(k, θ*)-bounding and a new anonymization method, Closed-MSpartition, to process SRS data with missing values. We used US FDA's FAERS data to evaluate our proposed method from the aspects of information loss, privacy risk, and data utility. The results show that our proposed new method can effectively prevent attackers from learning personal privacy without sacrificing data quality and utility.

International Journal of Fuzzy Systems, Vol. x, No. y, month and year An Active Multidimensional Association Mining Framework with User Preference Ontology

1 Business data are subject to change by time or by the modifications of business rules. New know... more 1 Business data are subject to change by time or by the modifications of business rules. New knowledge needs to be extracted to reflect the most up to date situations hence periodic or occasional re-mining is essential. This paper proposes an active multidimensional association mining framework that incorporates with user preference ontology, which contains surrogate queries that represent frequently used queries in the query history log. The representative power and the user preference of the surrogate queries are derived and expressed in fuzzy linguistic terms. The construction of the ontology is demonstrated. How it can assist the active mining mechanism is also described. Specifically, the connection of the user preference ontology to the user profile in the enterprise database allows dispatching of new mining results to specific users automatically. A prototype implementation of the proposed system framework is provided and an effectiveness experiment for the user preference on...

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Mining Frequent Patterns with Item, Aggregation, and Cardinality Constraints

2008 3rd International Conference on Innovative Computing Information and Control, 2008

Recently, the topic of constraint based association mining has received increasing attention with... more Recently, the topic of constraint based association mining has received increasing attention within the data mining research community. By allowing more user specified constraints other than traditional rule measurements, e.g., minimum support and confidence, research work on this topic endeavor to reflect real interest of analysts and relief them from the overabundance of rules, and ultimately, fulfill an interactive environment for association analysis. So far most work on constraint based frequent patterns (itemsets) mining has been single constraint oriented, i.e., only one specific type of constraint is considered. Surprisingly little research has been conducted to deal with multiple types of constraints. This paper is an investigation on this problem. Specifically, three types of constraints are considered, including item constraint, aggregation constraint, and cardinality constraint. We propose an efficient algorithm, MCFP (Multi Constrained Frequent Pattern mining) to accomplish the task of discovering frequent itemsets satisfying all three types of constraints. Experimental results show that our algorithm is significantly faster than the intuitive approach, post processing the generated frequent patterns against user specified constraints.

A Mediator Exploiting Approach for Mining Indirect Associations from Web Data Streams

2011 Second International Conference on Innovations in Bio-inspired Computing and Applications, 2011

Abstract Recently, the concept of indirect associations, a new type of infrequent patterns that i... more

Towards On-Line ADR Detection: An Innovative Data Cube-based Approach

Studies in health technology and informatics, 2013

The problem of Adverse Drug Reactions (ADRs) detection is a key issue in the pharmacovigilance co... more The problem of Adverse Drug Reactions (ADRs) detection is a key issue in the pharmacovigilance community. Although many related researches have been proposed in recent years, majority of them were concerned about the accuracy of the detected adverse drug reactions, very few paying attention to the performance issue of how to quickly accomplish the detection process. In this work, we propose the concept of multivalued-dimension contingency cube (MDC-cube) to facilitate multidimensional, fast and online detection of ADRs, especially those caused by drug interactions. Experiments conducted on the FDA AERS data set show that our MDC-cube based method is an order of magnitude faster than the state-of-the-art ABCM-MS method.

Incremental Mining of Generalized Association Rules under Classification Ontology and Support Constraint Update

Mining association rules from a large business database, has been recognized as an important topi... more Mining association rules from a large business database, has been recognized as an important topic in the data mining community. A method that can help the analysis of associations is the use of classification ontology (taxonomy) and the setting of parameter constraints, such as minimum support. In real world applications, however, the classification ontology cannot be kept static while new transactions are continuously added into the original database, and the analysts may also need to set a different support constraint from the original one while formulating a new query in discovering real informative rules. Efficiently updating the discovered generalized association rules to reflect the change with classification ontology, support constraint and new added transactions is a crucial task. In this paper, we examine this problem and propose a novel algorithm, called IMA_HOSU, which can incrementally update the discovered generalized association rules when the classification ontology updates with a renewed minimum support. Empirical evaluations show that our algorithm is faster than applying the contemporary generalized associations mining algorithms to the whole updated database.

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A flexible three-phased object-oriented data mining approach for sequential transactions

Recently, the object concept has been very popular and used in a variety of applications, especia... more Recently, the object concept has been very popular and used in a variety of applications, especially for complex data description. In the past, data mining is most commonly used in attempts to induce association rules from transaction data. As to object-oriented data mining, Huang et al proposed an algorithm for finding interand intra-class knowledge. This paper will improve it and propose a modified mining algorithm to derive sequential patterns from object-oriented data with more pruning effects. There are three kinds of knowledge to be discovered: sequential inter-class patterns, intra-class association rules and sequential inter-intra patterns. There are three phases in the algorithm, with each for a kind of knowledge. The results from the previous phase can be used to prune candidates in the current phase. The candidates generated will thus become much less than those without pruning. Besides, the approach can be easily modified to stop at an intermediate phase if only the desi...

An Efficient Data Mining Algorithm by Multi-Utility Minimum Support and Prefix-Search Strategy

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019

In recent years, how to discover the valuable knowledge from a huge amount of data has been a hot... more In recent years, how to discover the valuable knowledge from a huge amount of data has been a hot topic. Data mining is one of the solutions for this topic. Actually, data mining has been studied for a long time, including a lot of paradigms. Among these paradigms, High-Utility Itemset Mining attracts much research attention because it can find the itemsets different from traditional frequent itemsets. Although these related works have been shown to be efficient, it still cannot mine the really rare itemsets infrequent by only one minimum utility support. In addition, an efficient mining algorithm relies on an important factor “search strategy”. For these concerns, in this paper, an efficient high-utility itemset mining algorithm with multiple minimum utility support and prefix-search strategy is proposed to effectively mine the really valuable itemsets. For effectiveness, the rare but infrequent itemsets can be discovered by the individually specified utility supports. For efficien...

A Modified Three-Phased Object-Oriented Mining Approach for Association Rules

Journal of Internet Technology, 2008

Recently, the object concept has been very popular and used in a variety of applications, especia... more Recently, the object concept has been very popular and used in a variety of applications, especially for complex data description. In this paper, we propose an approach to find from objects three kinds of knowledge: inter-class association rules, intra-class association rules and inter-intra association rules. There are three phases in the algorithm, with each for a kind of knowledge. The results from the previous phase can be used to prune candidates in the current phase. The candidates generated will thus become much fewer than those without pruning. Besides, the approach can be easily modified to stop at an intermediate phase if only the desired kind of knowledge is to be obtained. An example is also given to illustrate the proposed algorithm.

An active multidimensional association mining framework with user preference ontology

International Journal of Fuzzy Systems, 2010

Business data are subject to change by time or by the modifications of business rules. New knowle... more Business data are subject to change by time or by the modifications of business rules. New knowledge needs to be extracted to reflect the most up to date situations hence periodic or occasional re-mining is essential. This paper proposes an active multidimensional association mining framework that incorporates with user preference ontology, which contains surrogate queries that represent frequently used queries in the query history log. The representative power and the user preference of the surrogate queries are derived and expressed in fuzzy linguistic terms. The construction of the ontology is demonstrated. How it can assist the active mining mechanism is also described. Specifically, the connection of the user preference ontology to the user profile in the enterprise database allows dispatching of new mining results to specific users automatically. A prototype implementation of the proposed system framework is provided and an effectiveness experiment for the user preference onto...

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Parallel sparse matrix ordering: quality improvement using genetic algorithms

Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)

ABSTRACT

Privacy Preserving Anonymity for Periodical Releases of Spontaneous ADE Reporting Data: Algorithm Development and Validation (Preprint)

BACKGROUND Increasingly, spontaneous reporting systems (SRS) have been established to collect adv... more BACKGROUND Increasingly, spontaneous reporting systems (SRS) have been established to collect adverse drug events to foster the research of ADR detection and analysis. SRS data contains personal information and so its publication requires data anonymization to prevent the disclosure of individual privacy. We previously have proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically, e.g., FAERS, to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, i.e., MS(k, θ*)-bounding. OBJECTIVE We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintain the utility of published data. METHODS We identify some potential att...

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Three Maintenance Algorithms for Compressed Object-Oriented Data Warehousing

International Journal of Computers and Applications, 2001

In this paper, the concept of object-oriented data warehousing is introduced and discussed. A new... more In this paper, the concept of object-oriented data warehousing is introduced and discussed. A new data model, called the compressed data model, is proposed for storing the data in the object-oriented data warehouse. The data model will form new classes according to the definitions of views, such that the query performance and security can be improved. Three incremental maintenance algorithms, including instance insertion, deletion, and update, are proposed to maintain consistency between the data warehouse and the source databases.

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Privacy preserving data anonymization of spontaneous ADE reporting system dataset

BMC Medical Informatics and Decision Making, 2016

Background: To facilitate long-term safety surveillance of marketing drugs, many spontaneously re... more Background: To facilitate long-term safety surveillance of marketing drugs, many spontaneously reporting systems (SRSs) of ADR events have been established worldwide. Since the data collected by SRSs contain sensitive personal health information that should be protected to prevent the identification of individuals, it procures the issue of privacy preserving data publishing (PPDP), that is, how to sanitize (anonymize) raw data before publishing. Although much work has been done on PPDP, very few studies have focused on protecting privacy of SRS data and none of the anonymization methods is favorable for SRS datasets, due to which contain some characteristics such as rare events, multiple individual records, and multi-valued sensitive attributes. Methods: We propose a new privacy model called MS(k, θ *)-bounding for protecting published spontaneous ADE reporting data from privacy attacks. Our model has the flexibility of varying privacy thresholds, i.e., θ * , for different sensitive values and takes the characteristics of SRS data into consideration. We also propose an anonymization algorithm for sanitizing the raw data to meet the requirements specified through the proposed model. Our algorithm adopts a greedy-based clustering strategy to group the records into clusters, conforming to an innovative anonymization metric aiming to minimize the privacy risk as well as maintain the data utility for ADR detection. Empirical study was conducted using FAERS dataset from 2004Q1 to 2011Q4. We compared our model with four prevailing methods, including k-anonymity, (X, Y)-anonymity, Multi-sensitive l-diversity, and (α, k)-anonymity, evaluated via two measures, Danger Ratio (DR) and Information Loss (IL), and considered three different scenarios of threshold setting for θ * , including uniform setting, level-wise setting and frequency-based setting. We also conducted experiments to inspect the impact of anonymized data on the strengths of discovered ADR signals. Results: With all three different threshold settings for sensitive value, our method can successively prevent the disclosure of sensitive values (nearly all observed DRs are zeros) without sacrificing too much of data utility. With non-uniform threshold setting, level-wise or frequency-based, our MS(k, θ *)-bounding exhibits the best data utility and the least privacy risk among all the models. The experiments conducted on selected ADR signals from MedWatch show that only very small difference on signal strength (PRR or ROR) were observed. The results show that our method can effectively prevent the disclosure of patient sensitive information without sacrificing data utility for ADR signal detection.

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Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms

Journal of Advanced Computational Intelligence and Intelligent Informatics, 2007

In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, i... more In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration rates on the performance and solution quality of MGAs. Thereby, we propose an adaptive scheme to adjust the appropriate migration rates for MGAs. If the individuals from a neighboring sub-population can greatly improve the solution quality of a current population, then the migration from the neighbor has a positive effect. In this case, the migration rate from the neighbor should be increased; otherwise, it should be decreased. According to the principle, an adaptive multi-population genetic algorithm which can adjust the migration rates is proposed. Experiments on the 0/1 knapsack problem are conducted to show the effectiveness of our approach. The results of our work have illustrated the effectiveness of self-adaptation for MGAs and paved the way for this unexplored area.

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