Abstract: The challenges of the standard clustering methods and the weaknesses of Apriori algorithm in frequent termset clustering formulate the goal of our research. Based on Association Rules Mining, an efficient approach for Web Document Clustering (ARWDC) has been devised. An efficient Multi-Tire Hashing Frequent Termsets algorithm (MTHFT) has been used to improve the efficiency of mining association rules by targeting improvement in mining of frequent termset. Then, the documents are initially partitioned based on association rules. Since a document usually contains more than one frequent termset, the same document may appear in multiple initial partitions, i.e., initial partitions are overlapping. After making partitions disjoint, the documents are grouped within the partition using descriptive keywords, the resultant clusters are obtained effectively. In this paper, we have presented an extensive analysis of the ARWDC approach for different sizes of Reuters datasets. Furthermore the performance of our approach is evaluated with the help of evaluation measures such as, Precision, Recall and F-measure compared to the existing clustering algorithms like Bisecting K-means and FIHC. The experimental results show that the efficiency, scalability and accuracy of the ARWDC approach has been improved significantly for Reuters datasets.

Noha Negm, Mohamed Amin, Passent Elkafrawy and Abdel Badeeh M. Salem, “Investigate the Performance of Document Clustering Approach Based on Association Rules Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 4(8), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040820

@article{Negm2013,
title = {Investigate the Performance of Document Clustering Approach Based on Association Rules Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040820},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040820},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {8},
author = {Noha Negm and Mohamed Amin and Passent Elkafrawy and Abdel Badeeh M. Salem}
}