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An Efficient Method for extracting Frequent Pattern Using Transposition of Database

Author(s): Sundar Birla

Journal: Journal of Current Engineering Research
ISSN 2250-2637

Volume: 2;
Issue: 6;
Date: 2012;
Original page

Keywords: LCS | Apriori algorithm | Frequent itemset | Data mining | Space complexity | transposition of database.

Apriori is a classical algorithm for frequent patterns extraction. Apriori is designed to operate on databases containing transactions. The purpose of the Apriori Algorithm is to find frequent itemsets between different transaction sets of data. The aim of this research is to improve the performance of the conventional Apriori algorithm that extracts frequent patterns for binary transaction dataset. An approach implemented in Transposed database then result is very fast. Recently, different works proposed a new way to mine frequent patterns in transposed databases where a database with thousands of attributes but only tens of objects. In this case, mining the transposed database runs through a smaller search space. This work systematically explores the search space of frequent patterns mining and represent database in transposed form. This paper proposed an algorithm for mining frequent patterns which are based on Apriori algorithm and used space reduced longest common sequence (LCS) which makes apriori algorithm space efficient. Space complexity for Proposed algorithm is O(n) while the Dynamic Approach like Longest Common Subsequence space complexity is O(n2) memory for given items in dataset
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