A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Hong, Tzung-Pei; Chen, Chun‐Hao; Wu, Yu-Lung; Lee, Yeong-Chyi

doi:10.1007/s00500-006-0046-x

Cited by 103 publications

(79 citation statements)

References 19 publications

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“…Our method assumes that the membership functions are known in advance. In [6,12] , we proposed some fuzzy learning methods to automatically derive the membership functions. In the future, we will attempt to dynamically adjust the membership functions in the proposed web-mining algorithm to avoid the bottleneck of membership function acquisition.…”

Section: Discussionmentioning

confidence: 99%

Mining Fuzzy Weighted Browsing Patterns from Time Duration and with Linguistic Thresholds

Hong¹,

Chiang²,

Wang³

2008

American J. of Applied Sciences

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World-wide-web applications have grown very rapidly and have made a significant impact on computer systems. Among them, web browsing for useful information may be most commonly seen. Due to its tremendous amounts of use, efficient and effective web retrieval has become a very important research topic in this field. Techniques of web mining have thus been requested and developed to achieve this purpose. In this research, a new fuzzy weighted web-mining algorithm is proposed, which can process web-server logs to discover useful users' browsing behaviors from the time durations of the paged browsed. Since the time durations are numeric, fuzzy concepts are used here to process them and to form linguistic terms. Besides, different web pages may have different importance. The importance of web pages are evaluated by managers as linguistic terms, which are then transformed and averaged as fuzzy sets of weights. Each linguistic term is then weighted by the importance for its page. Only the linguistic term with the maximum cardinality for a page is chosen in later mining processes, thus reducing the time complexity. The minimum support is set linguistic, which is more natural and understandable for human beings. An example is given to clearly illustrate the proposed approach.

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Section: Discussionmentioning

confidence: 99%

Mining Fuzzy Weighted Browsing Patterns from Time Duration and with Linguistic Thresholds

Hong¹,

Chiang²,

Wang³

2008

American J. of Applied Sciences

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“…III. THE MINING SCHEME An efficient way to generate the fuzzy association rules consists of learning the MFs a priori and then use the final best set of MFs to mine fuzzy association rules [11], [13], [14]. This way to work allows us to learn the most adequate context [20] for each fuzzy partition, which is necessary in different contextual situations (different applications).…”

Section: Preliminaries: the 2-tuples Linguistic Representationmentioning

confidence: 99%

“…To evaluate a determined chromosome we will use the fitness functions defined in [14]. Before (Cq) where L1 is the number of large 1-itemsets obtained by using the set of MFs in Cq.…”

Section: Chromosome Evaluationmentioning

confidence: 99%

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Genetic Learning of Membership Functions for Mining Fuzzy Association Rules

Alcalá

Alcalá‐Fdez

Gacto

et al. 2007

2007 IEEE International Fuzzy Systems Conference

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Abstract-Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions.Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic label membership functions.Based on the 2-tuples linguistic representation model, we present a new fuzzy data-mining algorithm for extracting both association rules and membership functions by means of an evolutionary learning of the membership functions, using a basic method for mining fuzzy association rules.

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“…Mining algorithms that can automatically derive both the appropriate membership functions and the fuzzy rules are thus required. Many approaches have thus been proposed for deriving membership functions [10,11,18,19,23,24].…”

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confidence: 99%

Finding Pareto-front Membership Functions in Fuzzy Data Mining

Chen¹,

Hong²,

Tseng³

2012

IJCIS

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Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very important. In the past, we proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions. In this paper, we adopt a more sophisticated multi-objective approach, the SPEA2, to find the appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions and the second one is the total number of large 1-itemsets derived. Experimental comparisons of the proposed and the previous approaches are also made to show the effectiveness of the proposed approach in finding the Pareto-front membership functions.

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A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Cited by 103 publications

References 19 publications

Mining Fuzzy Weighted Browsing Patterns from Time Duration and with Linguistic Thresholds

Mining Fuzzy Weighted Browsing Patterns from Time Duration and with Linguistic Thresholds

Genetic Learning of Membership Functions for Mining Fuzzy Association Rules

Finding Pareto-front Membership Functions in Fuzzy Data Mining

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