ASSOCIATION RULE MINING WITH FREQUENT ITEMSET
Abstract
I The aim is to speed up the association rule mining process regardless the algorithm used to this end, enabling the performance of efficient implementations to be enhanced The rising interest in data storage has made the data size to be exponentially increased, hamper the process of knowledge discovery from these large volumes of high- dimensional and heterogeneous data. In recent years, many efficient algorithms for mining data associations have been proposed, facingup time and main memory requirements. However, this mining process could still become hard when the number of items and records is extremely high. In this paper, the goal is not to propose newefficient algorithms but a new data structure that could be used by a variety of existing algorithms without modifying its original schema. The structure simplifies, reorganizes, and speeds up the data access by sorting data by means of a shuffling strategy based on the hamming distance, which achieve similar values to be closer, and considering both an inverted index mapping and a run length encoding compression.
Author
A.Deepika.A
T.Nithya
Download
