A novel ranking method of web search result using clustering and concordance count

In recent years, information on World Wide Web is exploding. Search Engine robots are used to search information on World Wide Web. However, a robot type search engine has a few problems. One problem is that, it is difficult for user to come up with an appropriate query for getting search results she/he intends. Moreover, it is difficult for users to understand contents of search results because a robot type search engine outputs too many search results in a long list format. To resolve these problems, many systems classify a robot type search engine results into clusters. Clusters are labeled and those labels are shown to the user. Cluster labels need to be appropriate words for the web site within the cluster. We have proposed a labeling method using concordance count. First, web search results are obtained by a query input, and the result is classified into clusters. We used our proposed method to assign proper labels to those clusters. To ensure that we use a novel method. We find the set of websites resulted from AND-query using an original query word and the cluster label. If this set and the member of the cluster are common, we say that the concordance count is high. If the concordance count is high, the cluster label is assigned high weight. Finally, we evaluate the accuracy of our proposed method by simulation experiments.