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Frequent Pattern Mining Using Graph Based Approach

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Author(s): Sanjay Patel | Ketan Kotecha

Journal: International Journal of Computer Science Research and Application
ISSN 2012-9564

Volume: 1;
Issue: 2;
Start page: 12;
Date: 2011;
Original page

Keywords: Data Mining | Ant Colony Optimization (ACO) | Frequent Pattern Mining

ABSTRACT
Data mining is an Artificial Intelligence (AI) powered tool that can discover useful information within a database. Efficient algorithms to find frequent patterns are important in data mining research. Frequent pattern mining required in many business applications such as market analysis, production control, Science exploration and group decision support systems etc. Several effective data structures, such as two-dimensional arrays, trees and tries have been proposed to collect candidate and frequent item-sets. It seems as the tree structure is most extractive for storing item-sets. The outstanding tree has been proposed so far is called FP-tree (Frequent Pattern) which is a prefix tree structure. Some advancement with the tree structure is called the CATS tree (Compressed Arranged Transaction Sequences). CATS Tree extends the idea of FP-Tree to improve storage compression and allow frequent pattern mining without generation of candidate item-sets. Ant Colony Optimization (ACO) is the emerging field of artificial intelligence. ACO has shown a tremendous performance to solve many real time problems like Travelling Salesman Problem, Packet routing in Network and so on. Here in this paper the representation of the database is shown as a Graph to solve the frequent pattern mining problem, which is a prime requirement to solve any problem by ACO.

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