Frequent pattern mining is the process of finding a

\r\npattern (a set of items, subsequences, substructures, etc.) that occurs

\r\nfrequently in a data set. It was proposed in the context of frequent

\r\nitemsets and association rule mining. Frequent pattern mining is used

\r\nto find inherent regularities in data. What products were often

\r\npurchased together? Its applications include basket data analysis,

\r\ncross-marketing, catalog design, sale campaign analysis, Web log

\r\n(click stream) analysis, and DNA sequence analysis. However, one of

\r\nthe bottlenecks of frequent itemset mining is that as the data increase

\r\nthe amount of time and resources required to mining the data

\r\nincreases at an exponential rate. In this investigation a new algorithm

\r\nis proposed which can be uses as a pre-processor for frequent itemset

\r\nmining. FASTER (FeAture SelecTion using Entropy and Rough sets)

\r\nis a hybrid pre-processor algorithm which utilizes entropy and roughsets

\r\nto carry out record reduction and feature (attribute) selection

\r\nrespectively. FASTER for frequent itemset mining can produce a

\r\nspeed up of 3.1 times when compared to original algorithm while

\r\nmaintaining an accuracy of 71%.<\/p>\r\n","references":"[1] R. Agrawal, T. Imielinski, Mining Association Rules between Sets of\r\nItems in Large Databases. SIGMOD 1993, pp. 207-216.\r\n[2] S. Chai, J. Yang, Y. Cheng, The Research of Improved Apriori\r\nAlgorithm for Mining Association Rules, International Conference on\r\nService Systems and Service Management, 2007, pp. 1-4.\r\n[3] J. Liang, Y. Qian, Information granules and entropy theory in\r\ninformation systems, Science in China Series F: Information Sciences,\r\nVol. 51, 2008, pp. 1427-1444.\r\n[4] Pawlak. Rough Sets: Theoretical Aspects of Reasoning About Data.\r\nDordrecht: Kluwer Academic. 1991.\r\n[5] Li-Juan, L. Zhou-Jun, A novel rough set approach for classification,\r\nIEEE International Conference on Granular Computing, 2006, pp. 349-\r\n352.\r\n[6] C. Hung, H. Purnawan, B,Kuo, Multispectral image classification using\r\nrough set theory and the comparison with parallelepiped classifier,\r\nGeoscience and Remote Sensing Symposium, 2007. IGARSS 2007.\r\nIEEE International, pp. 2052-2055.\r\n[7] R. Jensen and Q. Shen. Fuzzy-rough data reduction with ant colony\r\noptimization. Fuzzy Sets Systems, vol. 149, Issue No. 1, 2005, pp. 5\u201320.\r\n[8] Zengyou H, Xiaofei Xu, An Optimization Model for Outlier Detection\r\nin Categorical Data, Lecture Notes in Computer Science, Volume 3644,\r\n2005, pp. 400-409.\r\n[9] UCL Machine Learning Group.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 94, 2014"}