A study of pattern of data mining algorithms and performance tuning of temporal data mining based on frequent inter transaction itemsets discovery

Abstract

Data mining association rules from transaction occurs at different time dimensions is a difficult task to mining the data because of multi dimensional attributes, high computational complexity, very large database size. The reason behind all this is the number of potential association rules becomes extremely large after the boundary of transactions is broken 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. newlineA good number of the studies carried out on mining association rules mining. Within the past few years, numbers of studies have been published presenting new algorithms or improvements on existing algorithm to solve frequent item set mining. Some algorithms require a small amount of memory, but heavy disk access (such as FITI - like algorithms); others necessitate low I/O activity, but large amount of memory (such as FP-growth). However, the number of research papers on the inter-transaction mining problem is still few since it is a more challenging problem than intra-transaction mining. In present study, researcher has proposed an efficient framework for mining inter-transaction association rules from historical stock market data. newlinePresent study is planned to improve the theoretical understanding of the association rule mining task by in-depth study of inter-transaction stock price movement of companies to financial research community, money managers, fund managers, investors etc. The study is aimed to capture new types of patterns and identify the probability of new algorithmic and other issues. newline