Algorithms for clustering data
Algorithms for clustering data
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Using a generalized instance set for automatic text categorization
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Combining Statistical and Relational Methods for Learning in Hypertext Domains
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
A probabilistic relational approach for web document clustering
Information Processing and Management: an International Journal
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Document clustering is classifying a data set of documents into groups of closely related documents, so that its resulting clusters can be used in browsing and searching the documents of a specific topic. In most cases of such as application, a set of new documents are incrementally added to the data set and there can be a large variation in the number of words in each document. This paper proposes an incremental document clustering method for an incrementally increasing data set of documents. The normalized inverse document frequency of a word in the data set is introduced to cope with the variation of the number of words in each document. Furthermore, an average link method for document clustering instead of using one similarity measure used in two similarity measures: a cluster cohesion rate and a cluster participation rate. Furthermore, a category tree for a set of identified clusters is introduced to assist the incremental document clustering of newly added documents. In this paper, the performance of the proposed method is analyzed by a series of experiments to identify their various characteristics.