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Engineered assembly may be treated mathematically as a clustering problem, in which cluster analysis hinges critically on the relationships between objects and attributes. This paper explores an analogical approach, via the evaluation of relationships against conventional assembly means, to derive and reveal the necessary relationships in collaborative virtual assembly. The inherent relationships...
This paper first studies the methods of web documents mining and text clustering, and summaries the fuzzy clustering algorithms and similarity measure functions, then proposes a modified similarity function which can solve the problems of feature selection and feature extraction in high-dimensional space. Finally, this paper puts forward to a dynamic fluzzy clustering algorithm(DCFCM) by combining...
Most web text clustering is based on the space vector text representation model. This results in a high dimension in the terms; and it leads to an increase in time complexity and a loss of text semantics due to the fact that the semantic relationship of the terms is not considered. In this paper, a new approach is taken where a concept lattice is generated with text treated as object and terms of...
With the rapid development of web video application, video similarity search has become a hot research field in content-based video retrieval. Many efforts have been carried out to improve the effectiveness and efficiency of similarity search in large database. In order to solve two challenging problems: similarity measurement and search method, a novel efficient VSS approach is proposed in this paper...
In order to improve detection efficiency of on-line web news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements of news stream(such as date, place, person and so on) to help detection process, and this method decreases text similarity computation greatly. Experimental...
This paper discusses the usage of document clustering methods for topic detection of emergencies. Its main contribution is to apply the named entity of event-based framework to extract the feature terms of Web documents, exploit the TF-IDF method to weight the Web document characteristics of emergencies, and finally detect the hot topics through the FCM clustering algorithm. This method can reduce...
Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second,...
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....
Web search users complain of inaccurate results of the current search engines. Most of inaccurate results are from failing to understand user's search goal. This paper proposes a method to mine user's intentions and to build an intention map representing their information needs. It selects intention features from search logs obtained from previous search sessions on a given query and extracts user's...
Rapid progress of network arouses much attention on Internet public opinion, it is important to grasp the internet public opinion in time and understand the trends of their opinion correctly. Text mining plays a fundamental role in categorization and monitoring of internet public opinion, but internet public opinion is much more difficult than pure-text process because of their semi-structured characteristic...
Since the emergence of BLOG, it not only represents a new network technology, but also means the beginning of a new life style. How to utilize and mine the BLOG content which contains hidden sentiment and real-time update is a big challenge in the data-mining domain. As most of the existing method for network text's topic mining is achieved through clustering text's topic and label which are labeled...
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 proposes a novel weighted feature fusion in color face recognition (FR) to automatically annotate faces in personal videos. In the proposed FR method, multiple face images (belonging to the same subject) are clustered from a sequence of video frames. To facilitate a complementary effect on improving annotation performance, the grouped faces are combined using the proposed weighted feature...
Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, focuses in this domain shifted from traditional vector based document similarity for clustering to suffix tree based document similarity, as it offers more semantic representation of the text present in the document...
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,...
Binary semantic relation extraction is particularly useful for various NLP and Web applications. Currently Web-based methods and Linguistic-based methods are two types of leading methods for semantic relation extraction task. With a novel view on integrating linguistic analysis on local text with Web frequent information, we propose a multi-view co-clustering approach for semantic relation extraction...
This paper proposed a new method of web news summarization via soft clustering algorithm. It used search engine to extract relevant documents, and mixed query sentence into sentences set which segmented from multi-document set, then this paper adopted efficient soft cluster algorithm SSSC to cluster all the sentences. If the number of cluster which contains the query sentence is larger than or equal...
Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. User page visits are sequential in nature. In this paper we presented new Rough set Dbscan clustering algorithm which identifies the behavior of the users page visits, order of occurrence of visits. Web data Clusters are formed using the rough...
This paper presents a methodology for learning taxonomic relations from a set of documents that each explain one of the concepts. Three different feature extraction approaches with varying degree of language independence are compared in this study. The first feature extraction scheme is a language-independent approach based on statistical keyphrase extraction, and the second one is based on a combination...
Extracting useful information from user generated text on the web is an important ongoing research in natural language processing, machine learning, and data mining. Online tools like emails, news groups, blogs, and web forums provide an effective communication platform for millions of users around the globe and also provide an added advantage of anonymity. Millions of people post information on different...
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