An improved algorithm for the calculation of exact term discrimination values
Information Processing and Management: an International Journal
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
On feature distributional clustering for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A smarter process for sensing the information space
IBM Journal of Research and Development
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In this paper, a new text-clustering algorithm named Frequent Term Set-based Clustering (FTSC) is introduced. It uses frequent term sets to cluster texts. First, it extracts useful information from documents and inserts into databases. Then, it uses the Apriori algorithm based on association rules mining efficiently to discover the frequent items sets. Finally, it clusters the documents according to the frequent words in subsets of the frequent term sets. This algorithm can reduce the dimension of the text data efficiently for very large databases, thus it can improve the accuracy and speed of the clustering algorithm. The results of clustering texts by the FTSC algorithm cannot reflect the overlap of texts' classes. Based on the FTSC algorithm, an improved algorithm—Frequent Term Set-based Hierarchical Clustering algorithm (FTSHC) is given. This algorithm can determine the overlap of texts' classes by the overlap of the frequent words sets, and provide an understandable description of the discovered clusters by the frequent terms sets. The FTSC, FTSHC and K-Means algorithms are evaluated quantitatively by experiments. The results of the experiments prove that FTSC and FTSHC algorithms are more efficient than K-Means algorithm in the performance of clustering.