Improving Association Rule Mining By Defining A Novel Data Structure

1Student,PES’s Modern College Of Engineering,Pune 5. 2HOD,Computer Engineering Dept, PES’s Modern College Of Engineering, Pune 5. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In recent years, growth in digital data storage in rapidly increased due to ease of use and lower coast digital storage media. This data is high dimensional and heterogeneous in nature. The process of knowledge discovery is being affected due to high dimensional and heterogeneous data. This process can be abbreviated as association rule mining (ARM). Though, many association rule mining algorithms have been proposed in recent years to deal with large volume of data, the mining process under-performs when the data size is very large in terms of records. Hence the aim of this work is not to design a new algorithm for mining, but to design a new data structure to store data reliably .The original data is simplified, recognized and access time increased for that data, to meet up efficiency in terms of time and main memory requirements. Lower main memory requirements and faster data access are achieved by means of Shuffling, Inverted Index Mapping and Run Length Encoding. Hence the resulting data structure can be used along with the existing association rule mining algorithms to speed up mining and reducing main memory requirements, without changing original algorithms. This is further improved by replacing Run Length Encoding by Modified Run Length Encoding Algorithm for better memory utilization and efficiency of mining algorithms.