An Improved Fuzzy Clustering Method for Analyzing Clustering Quality in XML Web Data

Web extensible markup language (XML) data quality analysis using clustering is necessary in many real time web applications. In huge applications XML is the standard format to explore the web data. In existing approaches the XML web data is clustered by using Weighted Fuzzy C means (WFCM),LSI(Latent Semantic Indexing),LSI*(Enhanced Latent Semantic Indexing) and K-means clustering approach. The existing approach generates degrades the efficiency of the performance. The performance of the clustering is increased by the novelty technique Improved Fuzzy Clustering Method (I-FCM)in this article. I-FCM is the machine learning method that clusters the XML data better than the other existing methods. First, using keyword the features are extracted from the XML web repository. Next, cluster the extracted features using I-FCM approach. Finally, necessary performance parameters in the clustering of XML data are analyzed. This article used the platform MATLAB to demonstrate the proposed approach. For the experimental purpose the LF-AMAZON dataset is utilized. The parameters accuracy, purity, entropy, and search time is utilized to measure the performance of the novelty technique. This performance is compared with the existing approach. The output of the novelty work shows the better performance and robust result is generated than the existing method.