Using Novel IR Measures to Learn Optimal Cluster Structures for Web Information Retrieval

The Internet is a vast resource of information. Unfortunately, finding and accessing this information is often a very cumbersome task even with existing information platforms. Searching on the WWW suffers from the fact that almost every word is ambiguous to a certain degree in the information-rich environment of the Internet. Clustering search results is a way to solve this problem. This paper demonstrates how to employ novel Information Retrieval measures to derive optimal parametrizations for a cluster algorithm.