The week's pick
Random Articles
Reseach Article
A New Structural Approach for Mining Frequent Itemset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 71 - Number 9 |
| Year of Publication: 2013 |
| Authors: Khushpinder Singh |
10.5120/12389-8748
|
Khushpinder Singh . A New Structural Approach for Mining Frequent Itemset. International Journal of Computer Applications. 71, 9 ( June 2013), 39-42. DOI=10.5120/12389-8748
Abstract
The problem of mining high quality frequent substructures from a large collection of semi-structured data has recently attracted a lot of research. There are various efficient algorithms available for discovering frequent substructures in a large structured data, where both of the patterns and the data are modeled by labeled unordered trees. In this paper, a new structure based algorithm ENHANCE CLIQUE TREE is proposed which creates the unordered structure of itemsets and gives the perceptive representation of the mining results. It eliminates the limitation of FP-tree which always follows the ordered tree structure and its performance also degrades when the dataset is regularly updated. Here a VERIFY TREE ALGORITHM is added in the proposed algorithm which verifies that whether there is a need to restructure the tree or not when the itemsets are inserted or deleted. It improves the performance so that it will reduce unnecessary space and time used for restructure the tree when there is no need for it.
References
- Rakesh Aggarwal, Ramakrishnan Srikant, (1994) "Fast algorithm for mining association rule" Proceeding on 20th VLDB Conference Santiago, Chile.
- Nikolaj Tatti, Hannes Heikinheimo, (2008) "Decomposable families of Itemsets" Helsinki University of Technology, Finland.
- Arabinda Nanda, Saroj Kumar Rout, (2008) "Data Mining & Knowledge Discovery in Databases: An AI Perspective" Department of Computer Science, Gandhi Engineering college, Bhubaneswar.
- Jiawei Han jain pei, Yiwen Yin, Runying mao, (2004) "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach" Data Mining and Knowledge Discovery, 8, 53–87
- Sanjeev Rao, Priyanka Gupta, (2012) "Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm" IJCST Vol l. 3, Jan-March
- Akash Rajak, Mahendra Kumar Gupta, (2007) "Association Rule Mining: Applications in Various Areas" Krishna Institute of Engineering & Technology, Ghaziabad
- http://www. coolinterview. com/interview/36017
- 8)http://in. docsity. com/endocs/Introduction_to_WEKA_Part_1-Data_Warehouse-Lecture_Handout_
Index Terms
Keywords