Sharif Digital Repository

Recent years have witnessed an explosion in the availability of news articles on the World Wide Web. In addition, organizing the results of a news search facilitates the user(s) in overviewing the returned news. In this work, we have focused on the label-based clustering approaches for news meta-search engines, and which clusters news articles based on their topics. Furthermore, our engine for NEws meta-Search REsult Clustering (NeSReC) is implemented along. NeSReC takes queries from the users and collect the snippets of news which are retrieved by The Altavista News Search Engine for the queries. Afterwards, it performs the hierarchical clustering and labeling based on news snippets in a... 

Clustering Web data is one important technique for extracting knowledge from the Web. In this paper, a novel method is presented to facilitate the clustering. The method determines the appropriate number of clusters and provides suitable representatives for each cluster by inference from a Bayesian network. Furthermore, by means of the Bayesian network, the contents of the Web pages are converted into vectors of lower dimensions. The method is also extended for hierarchical clustering, and a useful heuristic is developed to select a good hierarchy. The experimental results show that the clusters produced benefit from high quality  

Supplier selection is a decision-making process to identify and evaluate suppliers for making contracts. Here, we use interval type-2 fuzzy values to show the decision makers' preferences and also introduce a new formula to compute the distance between two interval type-2 fuzzy sets. The performance of the proposed distance formula in comparison with the normalized Hamming, normalized Hamming based on the Hausdorff metric, normalized Euclidean and the signed distances is evaluated. The results show that the signed distance has the same trend as our method, but the other three methods are not appropriate for interval type-2 fuzzy sets. Using this approach, we propose a hierarchical... 

Clustering is one of the most important approaches for mining and extracting knowledge from the web. In this paper a method for clustering the web data is presented which using a Bayesian network, finds appropriate representatives for each of the clusters. Having those representatives, we can create more accurate clusters. Also the contents of the web pages are converted into vectors which firstly, the number of dimensions is reduced, and secondly the orthogonality problem is solved. Experimental results show about the high quality of the resultant clusters. © Springer-Verlag Berlin Heidelberg 2007  

Searching large image databases is a time consuming process when done manually. Current CBIR methods mostly rely on training data in specific domains. When source and domain of images are unknown, unsupervised methods provide better solutions. In this work, we use a hierarchical clustering scheme to group images in an unknown and large image database. In addition, the user should provide the current class assignment of a small number of images as a feedback to the system. The proposed method uses this feedback to guess the number of required clusters, and optimizes the weight vector in an iterative manner. In each step, after modification of the weight vector, the images are reclustered. We... 

World Wide Web is a huge information space, making it a valuable resource for decision making. However, it should be effectively managed for such a purpose. One important management technique is clustering the web data. In this paper, we propose some developments in clustering methods to achieve higher qualities. At first we study a new density based method adapted for hierarchical clustering of web documents. Then utilizing the hyperlink structure of web, we propose a new method that incorporates density concepts with web graph. These algorithms have the preference of low complexity and as experimental results reveal, the resultant clusters have high quality. © 2009 Elsevier B.V. All rights...