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Frequent Pattern Mining Algorithms Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 145 - Number 9 |
| Year of Publication: 2016 |
| Authors: Ritesh Giri, Ananta Bhatt, Aadhya Bhatt |
10.5120/ijca2016910763
|
Ritesh Giri, Ananta Bhatt, Aadhya Bhatt . Frequent Pattern Mining Algorithms Analysis. International Journal of Computer Applications. 145, 9 ( Jul 2016), 33-36. DOI=10.5120/ijca2016910763
Abstract
Frequent pattern mining is the most researched field in data mining. This paper provides comparative study of fundamental algorithms and performance analysis with respect to both execution time and memory usage. It also provides brief overview of current trends in frequent pattern mining and it applications. There are two categories of frequent pattern mining the algorithm, namely Apriori algorithm and Tree structure algorithm. The Apriori based algorithm uses generate and test strategy approach to find frequent pattern by constructing candidate items and checking their counts and frequency from transactional databases. The Tree structure algorithm uses a text only approach. There is no need to generate candidate item sets. Many tree based structures have been proposed to represent the data for efficient pattern discovery including FP-Tree, CAT-Tree, CAN-Tree, CP-Tree, and etc. Most of the tree based structure allows efficient mining with single scan over the database. In this paper, we describe the formatting guidelines for IJCA Journal Submission.
References
- Syed Khairuzzaman Tanbeer, Chowdhary Farhan Ahmed, Byeong-Soo Jeong and Y.W.Lee, CP-Tree: A Tree Structure for Single-Pass Frequent Pattern Mining ,In proceedings of 12th Pacific Asia Conference,2008.
- C. Borgelt. An Implementation of the FP- growth Algorithm. Proc. Workshop Open Software for Data Mining (OSDM’05 at KDD’05, Chicago,IL),1–5.ACMPress, New York, NY, USA 2005.
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