Research of text clustering based on fuzzy granular computing

The typical algorithm of text clustering is a ldquoHard Partitionrdquo one, Actually, Chinese text is better to treat with ldquoSoft Partitionrdquo for its diversity and largeness. The fuzzy-set theory supply a powerful analyzing tool to this ldquoSoft partitionrdquo. Traditional fuzzy text clustering methods mostly are getting the fuzzy equivalent matrix or fuzzy division by iterating the matrix of membership degree, huge storage space is necessary for that process. The text clustering based on fuzzy granular computing will work as: first provide a normalized distance function in the fuzzy granularity space of text set, then use the function to do a dynamic clustering work to text who has a less distance than granularity. Approved by the test, this method has such advantages on reducing the computing complexity and space complexity, suitable for the status that many samples need to be processed.