Generation of Concise Clusters Using Fuzzy Combined Pattern Association Rules

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Data mining is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Combined mining is one of the ordinary methods for analysing complex data for identifying complex knowledge. 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 this proposed work new patterns match technique to group association rules, based on the similar attributes, pattern matching clustering algorithm is used to cluster the rules. This research work is used to combine more number of rules with a conditional value. Based on the conditional value, the result will be declared whether the rules or cluster or not.

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Keywords

Combined Pattern, Association rules, clustering, Data sets, cluster reduction.