IJSR - International Journal of Scientific Research

Hitesh R Raval; Dr.Vikram Kaushik

Volume : III, Issue : II, February - 2014

Data Mining: Performance Tuning Of Temporal Data Mining Based On Frequent Inter–Transaction Itemsets Discovery

Hitesh R Raval, Dr. Vikram Kaushik

Abstract :

A good number of the studies carried out on mining association rules so far are on mining intra-transaction associations. Traditional association rules are mainly concerned about intra-transaction rules. Mining association rules from transactions occurred at different time series is a difficult task because of high computational complexity, very large database size, multi dimensional attributes. The reason behind all this is the number of potential association rules becomes very large after the boundary of transactions is oken which pose more challenges on efficient processing. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. We proposed framework called FrequentITARM on real datasets of historical end of day price and traded volume. Our approach is efficient preprocessing, pruning techniques to efficiently discover the rule. Proposed work also provides better in-depth study of inter-transaction stock price movement of companies to financial research community, money managers, fund managers, investors etc.