Identifying Interesting Itemsets for Positive and Negative Association Rule Mining on an Encoded Temporal Database

Vol.2 No.4

Year: 2008

Issue: April-June

Title: Identifying Interesting Itemsets for Positive and Negative Association Rule Mining on an Encoded Temporal Database 

Author Name: Balasubramanian C, Duraiswamy K, Chandrasekar C   

Synopsis:   

The principle of data mining is better to use complicative primitive patterns and simple logical combination than simple primitive patterns and complex logical form. This paper overviews the concept of temporal database encoding, association rules mining. It proposes an innovative approach of data mining to reduce the size of the main database by an encoding method which in turn reduces the memory required. The use of the anti-Apriori algorithm reduces the number of scans over the database. A graph based approach uses Apriori for temporal mining. Also a basic algorithm using pruning facility for identifying potentially frequent and infrequent interesting item sets, and thereafter positive and negative association rule mining are focused. The objective involved is to obtain lower complexities of computations involved, time and space with effective identification of interesting itemsets, association rule mining.