Efficient Application of Association Rule Mining Algorithms on an Encoded Temporal Database with Weighted Items

摘要

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 for identifying frequent large item sets is focused. Also a method of mining association rules from an encoded database with weighted items is proposed. The objective of the proposed work is to obtain lower complexities of time and space.