A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules

Zhang, Shichao; Lu, Jingli; Zhang, Chengqi

doi:10.1016/j.ins.2003.09.017

Cited by 17 publications

(6 citation statements)

References 30 publications

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“…This value depends greatly on the problem to be solved, and its choice is a problem that is still not completely solved. In [49], a method based on fuzzy logic for the setting of the minimum confidence level is described.…”

Section: B Iterative Rule Extraction Modelmentioning

confidence: 99%

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

Jesus

González

Herrera

et al. 2007

IEEE Trans. Fuzzy Syst.

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Abstract-This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragón, Spain, in which it is necessary to extract automatically relevant and interesting information that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.

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Section: B Iterative Rule Extraction Modelmentioning

confidence: 99%

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

Jesus

González

Herrera

et al. 2007

IEEE Trans. Fuzzy Syst.

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show abstract

“…A problem for both attribute generalization and association rule extraction is in the user specification of metrics for thresholds, support counts, etc. Recent research has developed approaches using fuzzy logic to allow a more user-oriented specification for such measures [15,53,55].…”

Section: Data Miningmentioning

confidence: 99%

Fuzzy sets in database and information systems: Status and opportunities

Bosc

Kraft

Petry

2005

Fuzzy Sets and Systems

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“…This value depends greatly on the problem to be solved and its solution is a problem which is still not completely resolved. Zhang et al describes in [63] a method based on fuzzy logic for the setting of the minimum confidence level.…”

Section: Represents New Examples Endmentioning

confidence: 99%

Evolutionary Induction of Descriptive Rules in a Market Problem

Jesus

González

Herrera

et al. 2005

Intelligent Data Mining

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Abstract. Nowadays, face to face contact with the client continues to be fundamental to the development of marketing acts. Trade fairs are, in this sense, a basic instrument in company marketing policies, especially in Industrial Marketing. Due to the elevated investment in term of both time and money it is necessary the automatic extraction of relevant and interesting information which helps to improve fair planning policies.In this paper, we analyse this problem and the kind of knowledge the user is interested in. We study the use of Soft Computing methodologies, specifically Fuzzy Logic and Genetic Algorithms, in the design of the Data Mining algorithms most proper to this problem, descriptive induction algorithms for subgroup discovery. Then we present an evolutionary model for the descriptive induction of fuzzy or crisp rules which describe subgroups. The proposal includes a GA in an iterative model which extracts rules while examples are left uncovered and the rules obtained surpass an specified confidence level.

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A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules

Cited by 17 publications

References 30 publications

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

Fuzzy sets in database and information systems: Status and opportunities

Evolutionary Induction of Descriptive Rules in a Market Problem

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