Web Searching Based on Clustering Approach

Abstract

The dynamic web has increased exponentially over the past few years with more than thousands of documents related to a subject available to user now. Most of the web documents are unstructured and not in organized manner and hence user facing more difficult to find relevant documents. A more useful and efficient mechanism is combining clustering with ranking, where clustering can group the similar documents in one place and ranking can be applied to each cluster for viewing the top document at the beginning. This paper is proposed tf-idf based MLTransTrie (Multiple level Association Rule, Transposed Database,Trie) algorithm for clustering the web document. We then ranked the documents in each cluster using tf-idf and similarity factor of documents based on the user query. This approach will help the user to get all his relevant document in one place.