A New Tree StructureTo Extract Frequent Pattern
Priyanka Trikha2013
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Abstract
Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. Finding a frequent pattern (or items) plays as essentials role in data mining. Efficient algorithm to discover frequent patterns is essential in data mining research. A number of research works have been published that presenting new algorithm or improvements on existing algorithm to solve data mining problem efficiently. In that Apriori algorithm is the first algorithm proposed in this field. By the time of change or improvement in Apriori algorithm, the algorithms that compressed large database in to small tree data structure like FP tree, CAN tree and CP tree have been discovered. These algorithms are partitioned based , divide and conquer method used that decompose mining task in to smaller set of task for mining confined patterns in conditional database, which dramatically reduce search space. In this paper I propose a new novel tree structure - extension of CP tree...
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The use of Data mining is increasing very rapidly as daily analysis of transaction database consisting of data is increasing. In that data, there ae various item which occur frequently in same pattern. In data mining there are large number of algorithm which are available and used for finding the frequent pattern. In the existing system the algorithm used are Apriori and FP-Growth. The result obtained from such algorithm are very time consuming and not efficient. In proposed system we are using more compact data structure named Compressed FP Tre. We proposed a new algorithm CT-PRO which uses the Compressed FP Tree. The result of the proposed algorithm is much more efficient in terms of performance.
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