Abstract:

The text clustering method based on ontology applies WordNet and key concept set during text reprensentation, and the concept nodes and the semantic relations between the concepts in the ontology WordNet are used to reduce the number of features so as to improve clustering effect. And during text clustering, the algorithm uses the key concept set and the concept feature vector to calculate the similarity and uses key concept set to provide an explanation for every cluster of the result. The experimental results show that the method can effectively reduce the dimension number of the text feature vector and im
prove the text clustering effect compared with other text clustering algorithm and the novel method for text clustering can come up with a good explanation for the clusters.

Key words: ontology, WordNet, key concept set, concept feature vector