An Enhanced Approach for Compress Transaction Databases
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Date
2012
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Abstract
Associative rule mining is defined as the task that deals with the extraction of hidden knowledge
and-frequent patterns from very large databases. Traditional associative mining processes are iterative, time
consuming and storage expensive. To solve these processes, a way of representation that reduces this size
and at the same time maintains all the important and relevant data needed to extract the desired knowledge
from transaction databases is needed. This paper proposes a method that merges the transactions in the
transaction database and uses FP-Growth algorithm for mining associative knowledge is presented. The
experimental results in terms of compression ratio, both in terms of storage required and number of
transactions, prove that the proposed algorithm is an improved version to the existing systems.