An Efficient Gms Mechanism Based on Abstraction Features

In the traditional clustering mechanism there are number of irrelevant clusters are generating due to due to insufficient pre request .In our proposed mechanism called genetic based segmentation mechanism by using advanced clustering metrics. This method enhances the previous method of cluster the scientific documents based on abstraction features, so called GMS Clustering algorithm. There are 5 kinds of abstraction features of documents are defined, including body, abstract, subtitle, keyword and title. The mechanism of crossover and mutation in genetic algorithm is used to adjust the value of k and cluster center in the k-means algorithm dynamically. Experimental result supports our approach as better concept. The main aim of this paper is to eliminate the redundant documents and set priority to each document in the cluster. In the five Abstraction features, the clustering accuracy and steadiness of subtitle are only less than that of body, but the efficiency is much better than body because the subtitle size is much less than body size. The accuracy of clustering by combining subtitle and keyword is better than each of them individually, but is a little less than that by combining subtitle, keyword and body. If the efficiency is an essential factor, clustering by combining subtitle and keyword can be an optimal choice. The proposed system outperforms than the previous system. ----------------------------------------------------------------------***------------------------------------------------------------------------