Yahoo!Clusty1 is a Clustering Meta-search Engine (MSE) that allows users to send queries to Yahoo!. The returned snippets are grouped into homogeneous groups by topic. The objective of this project has been to create a flexible MSE for the Yahoo! web search engine. The purpose is to present the results returned to a query in a more structured format which will allow the user to easily explore t...

This paper describes Armil, a meta-search engine that groups the Web snippets returned by auxiliary search engines into disjoint labelled clusters. The cluster labels generated by Armil provide the user with a compact guide to assessing the relevance of each cluster to his/her information need. Striking the right balance between running time and cluster well-formedness was a key point in the de...

Many companies search the Web to learn about their competition and understand their potential customers. But how accurate are these search results? For instance, have you ever submitted the query "SAS", only to get results back about "Scandinavian Airline Systems"? This paper presents a SAS-based solution to accessing and clustering Yahoo! search engine results by using SAS Text Miner. We demon...

This chapter reports the results of a project attempting to assess the performance of a few major search engines from various perspectives. The search engines involved in the study include the Microsoft Search Engine (MSE) when it was in its beta test stage, AllTheWeb, and Yahoo. In a few comparisons, other search engines such as Google, Vivisimo are also included. The study collects statistics...

This paper is an introduction to the problem of search results clustering. SRC can be considered part of Web Mining, a dynamically growing branch of Information Retrieval. In this paper we give a definition of the problem, and compare it to classical document clustering. We summarize the existing body of knowledge in the field and discuss the problems with existing algorithms when applied to th...