CLASSIFICATION OF TEXT DATA USING FEATURE CLUSTERING ALGORITHM

Avinash Guru, Asma Parveen

Abstract: Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. Generally clustering means the collection of similar objects or data in groups. In this paper, we propose a feature clustering algorithm for classifying the text data. The document set contains number of words; these words are grouped into clusters based on the similarity. Words that are similar to each other are grouped into the same cluster, and the words that are not similar are grouped in another cluster. Each cluster is characterized by a membership function with statistical mean and deviation. When all the words are fed in the document then the clusters are formed automatically. Then the extracted feature starts functioning as it is based on the weighted combination of the words. By this algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Earlier, the user has to specify the extracted feature in advance but now it is not required as the clusters are formed automatically and the trial and error method can be avoided. The experimental results show that our method can run faster and obtain better extracted features than other methods.

Keywords: Feature clustering, feature extraction, feature reduction, text classification.

DOI: https://doi.org/10.15623/ijret.2014.0315062