Hybrid Active Feature Selection For Text Classification

Clustering is the most common form of unsupervised learning.In clustering, it is the distribution and makeup of the data that will determine cluster membership. It needs representation of objects and similarity measure. which compares distribution of features between objects. For the high dimensionality, feature extraction and feature selection improves the performance of clustering algorithms.In this paper, we describe the hybrid method used for text clustering which is the combination of active feature selection,genetic algorithm and bisecting K-means.External quality measures computes the effectiveness of clustering.Our hybrid method is compared with K-means.