An Efficient Approach for Discovering Closed Frequent Patterns in High Dimensional Data Sets
The growth in the new technology in the field of e-commerce and bioinformatics has resulted in production of large data sets with few new uniqueness. Microarray datasets consist of a very large number of features (nearly thousands of features) but very less number of rows because of its application type. ARM can be used to analyze such data and find the characteristics hidden in these data. However, most state-of-the-art ARM methods are not able to tackle a datasets containing large number of attributes effectively. In this paper, we have proposed and implemented a modified Carpenter algorithm with different consideration of data structure, which in result give us the better time complexity in compare to simple implementation of Carpenter.
KeywordsHigh Dimensional Data Association Rule Mining (ARM) Closed Frequent Pattern Frequent Pattern Microarray Data
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