{"title":"一种形式概念分析与文本挖掘相结合的基于概念的文档聚类方法","authors":"Nyeint Nyeint Myat, K. Hla","doi":"10.1109/WI.2005.1","DOIUrl":null,"url":null,"abstract":"Nowadays, the demand of conceptual document clustering is becoming increase to manage various types of vast amount of information published on the World Wide Web. In this paper, we use formal concept analysis (FCA) method for clustering documents according to their formal contexts. Concept hierarchy of documents is built using the formal concepts of the documents in the document corpus. We use tf.idf (term frequency /spl times/ inverse document frequency) term weighting model to reduce less useful concepts from these formal concepts and the association and correlation mining techniques to analyze the relationship of terms in the document corpus.","PeriodicalId":213856,"journal":{"name":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A combined approach of formal concept analysis and text mining for concept based document clustering\",\"authors\":\"Nyeint Nyeint Myat, K. Hla\",\"doi\":\"10.1109/WI.2005.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, the demand of conceptual document clustering is becoming increase to manage various types of vast amount of information published on the World Wide Web. In this paper, we use formal concept analysis (FCA) method for clustering documents according to their formal contexts. Concept hierarchy of documents is built using the formal concepts of the documents in the document corpus. We use tf.idf (term frequency /spl times/ inverse document frequency) term weighting model to reduce less useful concepts from these formal concepts and the association and correlation mining techniques to analyze the relationship of terms in the document corpus.\",\"PeriodicalId\":213856,\"journal\":{\"name\":\"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)\",\"volume\":\"96 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2005.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2005.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
摘要
目前,为了管理万维网上发布的各类海量信息,对概念文档聚类的需求越来越大。在本文中,我们使用形式概念分析(FCA)方法根据文档的形式上下文进行聚类。使用文档语料库中文档的正式概念构建文档的概念层次结构。我们用tf。Idf (term frequency /spl times/ inverse document frequency)术语加权模型,从这些形式化概念中减少不太有用的概念,并使用关联和相关性挖掘技术分析文档语料库中术语之间的关系。
A combined approach of formal concept analysis and text mining for concept based document clustering
Nowadays, the demand of conceptual document clustering is becoming increase to manage various types of vast amount of information published on the World Wide Web. In this paper, we use formal concept analysis (FCA) method for clustering documents according to their formal contexts. Concept hierarchy of documents is built using the formal concepts of the documents in the document corpus. We use tf.idf (term frequency /spl times/ inverse document frequency) term weighting model to reduce less useful concepts from these formal concepts and the association and correlation mining techniques to analyze the relationship of terms in the document corpus.