CQIG: An Improved Web Search Results Clustering Algorithm

Abstract

Massive linear search results returned from traditional search engines bring much inconvenience to users when extract the information they need. Search result clustering is of critical need for grouping similar topics of documents. The existing algorithm has drawbacks in clustering labels screening, cluster quality assessment, overlapping clusters controlling. The improved clustering algorithm-CQIG, which based on LINGO, improved the cluster and cluster label scoring function, increased the cluster merging process and improved the processing effect of Chinese. Finally, a recommended platform for Web search results clustering is established based on carrot2 framework to prove the accuracy, distinction and readability of CQIG.