A Tolerance Rough Set Approach to Clustering Web Search Results

Abstract

Extended AbstractTwo most popular approaches to facilitate searching for information on the web are represented by web search engine and web directories. Although the performance of search engines is improving every day, searching on the web can be a tedious and time-consuming task due to the huge size and highly dynamic nature of the web. Moreover, the user's "intention behind the search" is not clearly expressed which results in too general, short queries. Results returned by search engine can count from hundreds to hundreds of thousands of documents.One approach to manage the large number of results is clustering. Search results clustering can be defined as a process of automatical grouping search results into to thematic groups. However, in contrast to traditional document clustering, clustering of search results are done on-the-fly (per user query request) and locally on a limited set of results return from the search engine. Clustering of search results can help user navigate through large set of documents more efficiently. By providing concise, accurate description of clusters, it lets user localizes interesting document faster.In this paper, we proposed an approach to search results clustering based on Tolerance Rough Set following the work on document clustering [4,3]. Tolerance classes are used to approximate concepts existed in documents. The application of Tolerance Rough Set model in document clustering was proposed as a way to enrich document and cluster representation with the hope of increasing clustering performance.Tolerance Rough Set Model: (TRSM) was developed in [3] as basis to model documents and terms in information retrieval, text mining, etc. With its ability to deal with vagueness and fuzziness, TRSM seems to be promising tool to model relations between terms and documents. In many information retrieval problems, defining the similarity relation between document-document, term-term or termdocument is essential.Let D = {d 1 , . . . , d N } be a set of documents and T = {t 1 , . . . , t M } set of index terms for D. TRSM is an approximation space (see [5]) R = (T, I θ , ν, P ) determined over the set of terms T (universe of R) as follows:

A survey of Web clustering engines

“…Another interesting algorithm that is inherently data-centric, but tries to create features comprehensible for users and brings us closer to the next section, is called tolerance rough set clustering (TRSC) [Ngo and Nguyen 2004]. In TRSC, the bag-of-words document representation is enriched with features possibly missing in a document and calculated using a tolerance rough set model computed over the space of all of the collection's terms.…”

Meaningless to meaningful Web log data for generation of Web pre-caching decision rules using Rough Set

“…In this case, to answer that question, Ngo and Nguyen [16] proposed an approach to search for results clustering based on tolerance RS model following the work on document clustering. The application of tolerance RS model in document clustering was proposed as a way to enrich document and cluster representation to increase clustering performance.…”

An Independent Rough Set Approach Hybrid with Artificial Bee Colony Algorithm for Dimensionality Reduction

“…Dimensionality Reduction or Feature subset selection is one of the important steps in data mining (Ahmed et al, 2009;Ngo and Nguyen, 2009;Selamat et al, 2010;Shylaja et al, 2010). Numerous features have been acquired and stored in databases due to the growth and development in real-time applications.…”

“…It is essential to reduce the dimensionality by selecting most relevant features which results in decreasing the measuring cost, transmission and storage cost and compact classification models. There are several techniques that have been proposed in the literature: Filter, wrapper and embedded (Selamat et al, 2010), unsupervised (Shylaja et al, 2010) and supervised (Ngo and Nguyen, 2009;Jensen and Shen, 2007) Rough set theory provides a mathematical tool that can be used for both feature selection and knowledge discovery (Jensen and Shen, 2007). It helps us to find out the minimal attribute sets called 'reducts' to classify objects without deterioration of classification quality and induce minimal length decision rules inherent in a given information system.…”