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A Performance Based Transposition algorithm for Frequent Itemsets Generation
Sanjeev Kumar Sharma, Ugrasen Suman
Pages - 53 - 61     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 2   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Data Mining, Association Rule Mining(ARM), Association Rules
ABSTRACT
Association Rule Mining (ARM) technique is used to discover the interesting association or correlation among a large set of data items. it plays an important role in generating frequent itemsets from large databases. Many industries are interested in developing the association rules from their databases due to continuous retrieval and storage of huge amount of data. The discovery of interesting association relationship among business transaction records in many business decision making process such as catalog decision, cross-marketing, and loss-leader analysis. It is also used to extract hidden knowledge from large datasets. The ARM algorithms such as Apriori, FP-Growth requires repeated scans over the entire database. All the input/output overheads that are being generated during repeated scanning the entire database decrease the performance of CPU, memory and I/O overheads. In this paper, we have proposed a Performance Based Transposition Algorithm (PBTA) for frequent itemsets generation. We will compare proposed algorithm with Apriori algorithm for frequent itemsets generation. The CPU and I/O overhead can be reduced in our proposed algorithm and it is much faster than other ARM algorithms.
CITED BY (10)  
1 Singh, A. K., Tondon, S. R., & Diwan, T. D. One Time Mining by Multi-Core Preprocessing on Generalized Dataset.
2 Sharma, S. K., & Ugrasen, S. (2014). A trust-based architectural framework for collaborative filtering recommender system. International Journal of Business Information Systems, 16(2), 134-153.
3 Sharma, S. K., & Suman, U. (2013). A framework of hybrid recommender system for web personalisation. International Journal of Business Information Systems, 13(3), 284-316.
4 Sharma, S. K., & Suman, U. (2013). An efficient semantic clustering of URLs for web page recommendation. International Journal of Data Analysis Techniques and Strategies, 5(4), 339-358.
5 Verma, G., & Nanda, V. (2012). Association Rule Mining by Block Scattered Transposition. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(3), pp-99.
6 Sharma, S. K., & Suman, U. (2012, September). Comparative study and analysis of web personalization frameworks of recommender systems for e-commerce. In Proceedings of the CUBE International Information Technology Conference (pp. 629-634). ACM.
7 Verma, G., & Nanda, V. (2012, March). An Effectual Algorithm For Frequent Itemset Generation In Generalized Data Set Using Parallel Mesh Transposition. In Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on (pp. 719-724). IEEE.
8 Verma, G., & Nanda, V. (2012). Frequent Item set Generation by Parallel Preprocessing on Generalized Dataset. International Journal of Scientific & Engineering Research, 3(4).
9 Sharma, S. K., & Suman, U. (2011). Design and Implementation of Architectural Framework of Recommender System for e-Commerce. International Journal of Computer Science and Information Technology & Security (IJCSITS), 1(2), 153-162.
10 Sharma, S. K., & Suman, U. (2011). Analysis of Frequent URLs for a Recommender System Using Performance Based Transposition Algorithm. Automation and Autonomous System, 3(11), 526-532.
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Mr. Sanjeev Kumar Sharma
devi ahilya university indore madhya pradesh - India
spd50020@gmail.com
Dr. Ugrasen Suman
Devi Ahilya University, Takshashila Campus,Khandwa Road Indore (M.P.) India - India