Detection of Fuzzy Association Rules by Fuzzy Transforms

Martino, Ferdinando Di; Sessa, Salvatore

doi:10.1155/2012/258476

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…The approach combines the obtained partitions with predefined linguistic labels. Many algorithms for determining suitable fuzzy partitions and finding rules on the basis of heuristic optimization, using genetic algorithms and other techniques, can be found in the literature …”

Section: Finding Associations In Fuzzy Transactionsmentioning

confidence: 99%

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Marín

Ruiz

Sánchez

2016

WIREs Data Min & Knowl

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Looking for associations in data is one of the data mining tasks that has aroused more interest in the literature. In this area, incorporating concepts of fuzzy set theory is useful in problems where imprecision and/or uncertainty appear. In most of the existing approaches, fuzzy association rules are widely seen as fuzzy rules, which are very different in nature from association rules, so problems like fuzzy inclusion and cardinality have been seldom taken into account explicitly, mostly providing ad hoc solutions for capturing semantics. In contrast, in this study we have taken the more general and natural approach of considering the elements of the association rule mining framework and studying possible and sensible fuzzy extensions, referred here as fuzzy frameworks for mining associations. As fuzzy frameworks are abstract mathematical models, another key contribution of the article is the notion of interpretations as mappings between fuzzy frameworks and specific datasets. This general analysis of the field is completed with a study of various important aspects that arise when proposing quality measures in a fuzzy environment, as well as those related to computational issues. The work also includes a review of the fuzzy framework that arises from fuzzy transactions regarded as fuzzy subsets of items, and shows that many of the approaches on fuzzy association rules that exist in the literature can be placed in the context of a proper interpretation of that framework. WIREs Data Mining Knowl Discov 2016, 6:50–69. doi: 10.1002/widm.1176 This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Association Rules

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Section: Finding Associations In Fuzzy Transactionsmentioning

confidence: 99%

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Marín

Ruiz

Sánchez

2016

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…Sankaradass and Arputharaj (2011) Proposed to improve the intelligence assistance in analysis and even the fuzzy will be suitable for multidimentional data analysis. Martino and Sessa (2012) proposed a fuzzy mining approach to handle numerical data in databases with attributes and derived fuzzy association rules. At nearly the same time, Hong et al (1987) proposed a fuzzy mining algorithm to mine fuzzy rules from quantitative transaction data (Lee et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

Combined Pattern Mining for D3m Using Fuzzy

Dhenakaran

Maheswari

2013

American Journal of Applied Sciences

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Many algorithms and models were developed but the findings are not actionable and lack of soft power while solving the complex problems. Domain Driven Data mining is used a major efforts to promote the action ability of the knowledge discovery in the real world smart decision making. Combined mining is one of the common methods for analyzing complex data for identifying complex knowledge. The deliverables of combined mining are combined patterns. The complex environment gives the combined patterns. In this research we process a new technique called Fuzzy Combined Pattern Mining (FCPM) for Domain Driven Data Mining. It was used to find all the rules that satisfy the minimum support and minimum confidence constraints. In FCPM, we first apply the fuzzy concept to find the patterns after that the fuzzy pattern will be merged to combined pattern mining. The proposed algorithm have been implemented and compared with Apriori Its performance was studied on an experimental basis. The main objective is to provide the interesting patterns to the end user. The implementation of fuzzy in combined mining will generate the rules and based on rules we can identify the interesting patterns.

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