A Comparative Study to Select a Soft Computing Model for Developing the Knowledge Base of Data mining with Association Rule Formation by Factor Analysis

Dharmpal Singh

Abstract


Data Mining is a process of looking for unknown relationships and patterns and extracting useful information volumes of data in data warehouse. Mining means to abstract the information or mode which is implicit, unknown and valuable in large database or data warehouse.  Now a day, association rules mining from large databases is an active research field of data mining motivated by many application areas. Mining association rules (knowledge) is to searching out all existing valuable relationship of items from the given database with the statistics principle. Therefore, we have introduced the new measure criterion, which is factor analysis, to association rules mining, and used it to reduce the amount of rules in data mining. After getting the rules, applied the soft computing model to find the minimum error and based on it select the model which is used for knowledge discovery in Data mining.


Keywords


Fuzzy Logic, Particle swarm optimization, Nueral Network

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