Template Extraction from Heterogeneous Web Pages Using Text Clustering

Now a days most of the information is stored in text databases. This information consists of large collection of documents from Heterogeneous web pages. Now we extract template from these heterogeneous templates, and to extract template we use different algorithms to find similarity of underlying template structures in the documents and we cluster the web documents based on the similarity of underlying template structure in the documents so that template is extracted with various clusters. We use different algorithms to find similarity between the web pages. Previously the algorithms used are RTDM, Text-Hash and Text-Max. But the time and space occupied by this algorithms is more. In this paper we are using WaveK-Means algorithm to find similarity between the web pages. This algorithm provides better performance compared to previous algorithms in terms of space and time. The space and time consumed by this algorithm is less compared to RTDM, Text-Hash and Text-Max. Our Experimental results with real life data sets confirm effectiveness and robustness of our algorithm.