Kakinada Institute of Engineering and Technology for Women
Abstract
Feature subset clustering is a powerful technique to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a similarity-based self-constructing algorithm for feature clustering with the help of K-Means strategy. The words in the feature vector of a document set are grouped into clusters, based on similarity test. Words that are similar to each other are grouped into the same cluster, and make a head to each cluster data sets. By the FAST algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. Experimental results show that our FAST algorithm implementation can run faster and obtain better-extracted features than other methods
