A vector space model for automatic indexing
Communications of the ACM
Efficiently Clustering Documents with Committees
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Document clustering based on vector quantization and growing-cell structure
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Subspace clustering of text documents with feature weighting k-means algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Text clustering using frequent itemsets
Knowledge-Based Systems
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Document clustering has the goal of discovering groups with similar documents. The success of the document clustering algorithms depends on the model used for representing these documents. Documents are commonly represented with the vector space model based on words or n-grams. However, these representations have some disadvantages such as high dimensionality and loss of the word sequential order. In this work, we propose a new document representation in which the maximal frequent sequences of words are used as features of the vector space model. The proposed model efficiency is evaluated by clustering different document collections and compared against the vector space model based on words and n-grams, through internal and external measures.