Abstract: Document Clustering is one of the main themes in text mining. It refers to the process of grouping documents with similar contents or topics into clusters to improve both availability and reliability of text mining applications. Some of the recent algorithms address the problem of high dimensionality of the text by using frequent termsets for clustering. Although the drawbacks of the Apriori algorithm, it still the basic algorithm for mining frequent termsets. This paper presents an approach for Clustering Web Documents based on Hashing algorithm for mining Frequent Termsets (CWDHFT). It introduces an efficient Multi-Tire Hashing algorithm for mining Frequent Termsets (MTHFT) instead of Apriori algorithm. The algorithm uses new methodology for generating frequent termsets by building the multi-tire hash table during the scanning process of documents only one time. To avoid hash collision, Multi Tire technique is utilized in this proposed hashing algorithm. Based on the generated frequent termset the documents are partitioned and the clustering occurs by grouping the partitions through the descriptive keywords. By using MTHFT algorithm, the scanning cost and computational cost is improved moreover the performance is considerably increased and increase up the clustering process. The CWDHFT approach improved accuracy, scalability and efficiency when compared with existing clustering algorithms like Bisecting K-means and FIHC.

Noha Negm, Passent Elkafrawy, Mohamed Amin and Abdel Badeeh M. Salem, “Clustering Web Documents based on Efficient Multi-Tire Hashing Algorithm for Mining Frequent Termsets” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(6), 2013. http://dx.doi.org/10.14569/IJARAI.2013.020602

@article{Negm2013,
title = {Clustering Web Documents based on Efficient Multi-Tire Hashing Algorithm for Mining Frequent Termsets},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.020602},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020602},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {6},
author = {Noha Negm and Passent Elkafrawy and Mohamed Amin and Abdel Badeeh M. Salem}
}