Abstract
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.
Highlights
Data mining is called as the knowledge discovery in databases is the non-trivial process of identifying the valid, novel, potentially useful and understandable knowledge in large scale data
Fuzzy Membership function to convert numerical data set to fuzzy data set
The Fuzzy Membership function is used to convert numerical data set to fuzzy data set and the fuzzy pattern can be identified from the fuzzy data
Summary
Data mining is called as the knowledge discovery in databases is the non-trivial process of identifying the valid, novel, potentially useful and understandable knowledge in large scale data. The process of data mining stops at pattern identification. As the fact goes, (1) many algorithms have been designed of which very few are repeatable and executable in the real world, (2) often many patterns are mined but a major proportion of them are either commonsense or of no particular interest to business and (3) end users generally cannot understand and take them over for business use. Thorough efforts are essential for promoting the action-ability of knowledge discovery in real world smart decision making. To this end, domain-driven Data Mining (D3M) has been proposed to tackle the above issues
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