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An Efficient Method for extracting Frequent Pattern Using Transposition of Database

Birla Sunderlal, Anju Singh

Abstract


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

Keywords


LCS, Apriori algorithm, Frequent itemset, Data mining, Space complexity, transposition of database.

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