A Fuzzy Based Approach to Text Mining and Document Clustering
This work addresses document clustering for text analysis, but it is incremental as it applies an existing fuzzy method to a standard task without major innovations.
The paper tackled document clustering by applying fuzzy logic to text mining, using fuzzy c-means to cluster documents into two categories based on word frequency features, resulting in clusters that could be identified by analyzing feature values and providing membership degrees.
Fuzzy logic deals with degrees of truth. In this paper, we have shown how to apply fuzzy logic in text mining in order to perform document clustering. We took an example of document clustering where the documents had to be clustered into two categories. The method involved cleaning up the text and stemming of words. Then, we chose m number of features which differ significantly in their word frequencies (WF), normalized by document length, between documents belonging to these two clusters. The documents to be clustered were represented as a collection of m normalized WF values. Fuzzy c-means (FCM) algorithm was used to cluster these documents into two clusters. After the FCM execution finished, the documents in the two clusters were analysed for the values of their respective m features. It was known that documents belonging to a document type, say X, tend to have higher WF values for some particular features. If the documents belonging to a cluster had higher WF values for those same features, then that cluster was said to represent X. By fuzzy logic, we not only get the cluster name, but also the degree to which a document belongs to a cluster.