The search engines are indispensable tools to find information amidst massive web pages and documents. A good search engine needs to retrieve information not only in a shorter time, but also relevant to the users’ queries. Most search engines provide short time retrieval to user queries; however, they provide a little guarantee of precision even to the highly detailed users’ queries. In such ca...

Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able to capture the underlying user navigational interest and content preference simultaneously. In this paper we will present a novel web co-clust...

In this article, we examine an algorithm for document clustering using a similarity graph. The graph stores words and common phrases from the English language as nodes and it can be used to compute the degree of semantic similarity between any two phrases. One application of the similarity graph is semantic document clustering, that is, grouping documents based on the meaning of the words in th...

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this paper, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it...

Different document representation models have been proposed to measure semantic similarity between documents using corpus statistics. Some of these models explicitly estimate semantic similarity based on measures of correlations between terms, while others apply dimension reduction techniques to obtain latent representation of concepts. This paper proposes new hybrid models that combine explici...

Abstract. Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. ...

This paper consider the problem of search engine that are not capable of retrieving appropriate result on query given. Most of the users are not able to give the appropriate query to get what exactly they wanted to retrieve. So the search engine retrieves a massive list of data, which are ranked by the page rank algorithm or relevancy algorithm or human judgment algorithm. If the relevant resul...

Abstract Extracting meaningful information from short texts like tweets has proved to be a challenging task. Literature on topic detection focuses mostly methods that try guess the plausible words describe topics whose number been decided in advance. Topics change according initial setup of algorithms and show consistent instability with moving one another one. In this paper we propose an itera...