Pub Date : 2012-10-04DOI: 10.1109/URKE.2012.6319573
Chunping Ouyang, Xiaohua Yang, Xiaoyun Li, Zhiming Liu
Web document clustering is one of the most important research branches of Clustering Analyzing. The objective of web document clustering is to meet the need of retrieving web document efficiently from massive information in Internet. Recently social tagging is the important form of document organization in web 2.0, and the tagging as a document descriptor is used to improve the effectiveness of web searching. But a web document usually belongs to various category of tagging, which may lead to the difficulty of browsing web document based on single tagging. This paper explores the use of Formal Concept Analysis (FCA) as mathematical tool to analyze the social tagging of web document, and presents a model for web document clustering based on tagging semantic. Furthermore, taking community web site Douban as an example, the model is applied to allow users to tag and serendipitously browse web document using Formal Concept Analysis.
{"title":"Formal concept analysis support for web document clustering based on social tagging","authors":"Chunping Ouyang, Xiaohua Yang, Xiaoyun Li, Zhiming Liu","doi":"10.1109/URKE.2012.6319573","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319573","url":null,"abstract":"Web document clustering is one of the most important research branches of Clustering Analyzing. The objective of web document clustering is to meet the need of retrieving web document efficiently from massive information in Internet. Recently social tagging is the important form of document organization in web 2.0, and the tagging as a document descriptor is used to improve the effectiveness of web searching. But a web document usually belongs to various category of tagging, which may lead to the difficulty of browsing web document based on single tagging. This paper explores the use of Formal Concept Analysis (FCA) as mathematical tool to analyze the social tagging of web document, and presents a model for web document clustering based on tagging semantic. Furthermore, taking community web site Douban as an example, the model is applied to allow users to tag and serendipitously browse web document using Formal Concept Analysis.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-08-01DOI: 10.1109/URKE.2012.6319571
Keying Liu, Rui Li, Fasong Wang
There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.
{"title":"Blind separation of dependent sources using Schweizer-Wolff measure","authors":"Keying Liu, Rui Li, Fasong Wang","doi":"10.1109/URKE.2012.6319571","DOIUrl":"https://doi.org/10.1109/URKE.2012.6319571","url":null,"abstract":"There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.","PeriodicalId":277189,"journal":{"name":"2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122033565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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