Human Performance on Clustering Web Pages (original) (raw)
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Impact of Similarity Measures on Web-page Clustering
Clustering of web documents enables (semi-)automated categorization, and facilitates certain types of search. Any clustering method has to embed the documents in a suitable similarity space. While several clustering methods and the associated similarity measures have been proposed in the past, there is no systematic comparative study of the impact of similarity metrics on cluster quality, possibly because the popular cost criteria do not readily translate across qualitatively different metrics. We observe that in domains such as YA-HOO that provide a categorization by human experts, a useful criteria for comparisons across similarity met-rics is indeed available. We then compare four popular similarity measures (Euclidean, cosine, Pearson correlation and extended Jaccard) in conjunction with several clustering techniques (random, self-organizing feature map, hyper-graph partitioning, generalized k-means, weighted graph partitioning), on high dimen-sionai sparse data representing web documents. Performance is measured against a human-imposed classification into news categories and industry categories. We conduct a number of experiments and use t-tests to assure statistical significance of results. Cosine and extended Jaccard similarities emerge as the best measures to capture human categorization behavior, while Euclidean performs poorest. Also, weighted graph partitioning approaches are clearly superior to all others.
Web Pages Clustering: A New Approach
—The rapid growth of web has resulted in vast volume of information. Information availability at a rapid speed to the user is vital. English language (or any for that matter) has lot of ambiguity in the usage of words. So there is no guarantee that a keyword based search engine will provide the required results. This paper introduces the use of dictionary (standardised) to obtain the context with which a keyword is used and in turn cluster the results based on this context. These ideas can be merged with a metasearch engine to enhance the search efficiency.
An Analysis of Web Document Clustering Algorithms
Evidently there is a tremendous increase in the amount of information found today on the largest shared information source, the World Wide Web. The process of finding relevant information on the web is overwhelming. Even with the presence of today's search engines that index the web it is difficult to wade through the large number of returned documents in a response to a user query. Furthermore, users without domain expertise are not familiar with the appropriate terminology thus not submitting the right query terms, leading to the retrieval of more irrelevant pages and the most relevant documents do not necessarily appear at the top of the query output sequence. Users of Web search engines are thus often forced to sift through the long ordered list of document " snippets " returned by the engines. This fact has lead to the need to organize a large set of documents into categories through clustering. The Information Retrieval community has explored document clustering as an alternative method of organizing retrieval results. Grouping similar documents together into clusters will help the users find relevant information quicker and will allow them to focus their search in the appropriate direction. Various web document clustering techniques are now being used to give meaningful search result on web. In this paper an analysis of the various categories of web document clustering and also the various existing web clustering engines with its relevant clustering techniques are presented.
Clustering Web Information Sources
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Abstract The explosive growth of the Web has drastically increased information circulation and dissemination rates. As the numbers of both Web users and Web sources grow significantly every day, crucial data management issues, such as clustering on the Web, should be addressed and analyzed. Clustering has been proposed toward improving both information availability and the Web users' personalization.
A Review of Web Document Clustering Approaches
Nowadays, the Internet has become the largest data repository, facing the problem of information overload. Though, the web search environment is not ideal. The existence of an abundance of information, in combination with the dynamic and heterogeneous nature of the Web, makes information retrieval a difficult process for the average user. It is a valid requirement then the development of techniques that can help the users effectively organize and browse the available information, with the ultimate goal of satisfying their information need. Cluster analysis, which deals with the organization of a collection of objects into cohesive groups, can play a very important role towards the achievement of this objective. In this paper, we present an exhaustive survey of web document clustering approaches available on the literature, classified into three main categories: text-based, link-based and hybrid. Furthermore, we present a thorough comparison of the algorithms based on the various facets of their features and functionality. Finally, based on the review of the different approaches we conclude that although clustering has been a topic for the scientific community for three decades, there are still many open issues that call for more research.
Clustering Web Search Results-A Review
The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications and personal homepages, etc. Effective representation of Web search results remains an open problem in the Information Retrieval (IR) community. Web search result clustering has been emerged as a method which overcomes these drawbacks of conventional information retrieval (IR) community. It is the clustering of results returned by the search engines into meaningful, thematic groups. This paper gives issues that must be addressed in the development of a Web clustering engine and categorizes various techniques that have been used in clustering of web search results.
Studies in Computational Intelligence, 2009
This chapter provides a survey of some clustering methods relevant to clustering Web elements for better information access. We start with classical methods of cluster analysis that seems to be relevant in approaching the clustering of Web data. Graph clustering is also described since its methods contribute significantly to clustering Web data. The use of artificial neural networks for clustering has the same motivation. Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering Web search results, in which clustering search engines arrange the search results into groups around a common theme. We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.
A survey of web clustering engines
ACM Computing Surveys …, 2009
Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.