DOI Number : 10.5614/itbj.ict.2007.1.1.4
Hits : 10

Discovery of Frequent Itemsets: Frequent Item Tree-Based Approach

A.V. Senthil Kumar1, R.S.D. Wahidabanu2

1Senior Lecturer, Department of MCA, CMS College of Science and Commerce

Coimbatore 641 006. Tamilnadu. India

2Head, Department of CSE, Govt. College of Engineering Salem, Tamilnadu, India

Abstract. Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of data, computational and I/O cost. Additionally, the recursive mining process to mine these structures is also too voracious in memory resources. In this paper, we describe a more efficient algorithm for mining complete frequent itemsets from transactional databases. The suggested algorithm is partially based on FP-tree hypothesis and extracts the frequent itemsets directly from the tree. Its memory requirement, which is independent from the number of processed transactions, is another benefit of the new method. We present performance comparisons for our algorithm against the Apriori algorithm and FP-growth.

Keywords: association rules; data mining; frequent items; frequent item tree; header table; minimum support

Download Article
Bahasa Indonesia | English


Begin on 10 October 2014 this website is no longer activated for article process in Journal of Mathematical and Fundamental Sciences, Journal of Engineering and Technological Sciences, Journal of ICT Research and Applications and Journal of Visual Art and Design. The next process will be proceeded under new website at

For detail information please contact us to:

       ITB Journal Visitor Number #19140055       
       Jl. Tamansari 64, Bandung 40116, Indonesia Visitor IP Address #       
       Tel : +62-22-250 1759 ext. 121 2011 Institut Teknologi Bandung       
       Fax : +62-22-250 4010, +62-22-251 1215 XHTML + CSS + RSS       
       E-mail : or Developed by AVE