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This article explores hierarchical clustering and graph analysis for detection groups in Wikis. Both approaches are explored in this work using historical information about Wiki pages. The results shows that this type of analysis can be used to identify people and groups with similar interests and abilities.
In Web 2.0 applications, users always label digital images using textual descriptions, which are also called tags. As a result, a web image usually carries both tag and visual content information. In order to improve the retrieval performance of web images, in this paper, we propose an error-driven fusion co-clustering algorithm, which combines images' tags, visual contents together for analysis....
The paper explores research concerned with improving aspects of the Web information gathering task. This type of task involves finding source of information on the Web, comparing different types of information, and re-finding information for reasoning and decision making. Research in Web information retrieval has explored visualization, clustering, and re-finding for improving the effectiveness of...
An approach to identification of the phishing target of a given (suspicious) webpage is proposed by clustering the webpage set consisting of its all associated webpages and the given webpage itself. We first find its associated webpages, and then explore their relationships to the given webpage as their features for clustering. Such relationships include link relationship, ranking relationship, text...
This paper compares the efficacy and efficiency of different clustering approaches for selecting a set of exemplar images, to present in the context of a semantic concept. We evaluate these approaches using 900 diverse queries, each associated with 1000 web images, and comparing the exemplars chosen by clustering to the top 20 images for that search term. Our results suggest that Affinity Propagation...
Keyword-based web search engine uses text to reflect users' query intentions. However, it is hard to descript user's intention with simple text terms accurately, and besides of this, it is also hard to make the association between the text terms and images precisely. As a result, the keyword-based image search engine may return large amount of junk images. In this paper, an interactive image filter...
We propose interest seam image, an efficient visual synopsis for video. To extract an interest seam image, a spatiotemporal energy map is constructed for the target video shot. Then an optimal seam which encompasses the highest energy is identified by an efficient dynamic programming algorithm. The optimal seam is used to extract a seam of pixels from each video frame to form one column of an image,...
Keeping track of news stories and events as they progress can be a tedious job, but as every day routine most of the web users read and follow many stories and events in news. If an analyst in her area has to follow and map all these according to the time-line they happen, the task quickly becomes overwhelming. We present an online tool which attempts to ease the analyst's task of finding all news...
The associations between different modalities of Web images could be very useful for Web image retrieval. In this paper, we investigate the multi-modal associations between two basic modalities of Web images, i.e. keyword and visual feature clusters, by data mining technique. The association rule crosses two modalities, in which the antecedent is a single keyword and the consequent is several visual...
This paper introduces a VIPS (Vision-based Page Segmentation) based Web mining method which aims to user intents based retrieval. It firstly grasps information from Web by making use of large search engines such as Baidu and so on, and then clusters the web pages basing on the intention-related features of Web text. The main algorithm is described in detail and experiments are designed to grasp the...
This paper proposes an efficient approach to find clusters of spatially related scene images collected from the website. Our method firstly builds a guide table, in which the ranked results are given according to the relevance scores of image pairs obtained by the image retrieval methods. Then the image clusters are generated by repeatedly choosing a seed image and performing query expansion directed...
An adaptive bottom up Web news extraction approach based on human perception is presented in this paper. The approach simulates how a human perceives and identifies Web news information by using an adaptive bottom up clustering strategy to detect possible news areas. It first detects news areas based on content function, space continuity, and formatting continuity of news information. It further identifies...
Concept-based multimedia search has become more and more popular in multimedia information retrieval (MIR). However, which semantic concepts should be used for data collection and model construction is still an open question. , there is very little research found on automatically choosing multimedia concepts with small semantic gaps. In this paper, we propose a novel framework to develop a lexicon...
TexPlorer is an integrated system for exploring and analyzing large amounts of text documents. The data processing modules of TexPlorer consist of named entity extraction, entity relation extraction, hierarchical clustering, and text summarization tools. Using a timeline tool, tree-view, table-view, and concept maps, TexPlorer provides an analytical interface for exploring a set of text documents...
The immaturity of semantic search engines has initiated researchers to apply various novel postprocessing techniques on traditional search engine results among which clustering routines are most conspicuous. While many of these routines are focused on hierarchical clustering, little has been done toward an effective visualization of such data. Due to the richness of information observed in 3D in comparison...
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