基于领域知识的有效片段聚类

2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications Pub Date : 2009-03-01 DOI:10.1109/DBKDA.2009.8

S. Patro, Wei Wang

{"title":"基于领域知识的有效片段聚类","authors":"S. Patro, Wei Wang","doi":"10.1109/DBKDA.2009.8","DOIUrl":null,"url":null,"abstract":"Clustering Web search result is a promising way to help alleviate the information overload for Web users. In this paper, we focus on clustering snippets returned by Google Scholar. We propose a novel similarity function based on mining domain knowledge and an outlier-conscious clustering algorithm. Experimental results showed improved effectiveness of the proposed approach compared with existing methods.","PeriodicalId":231150,"journal":{"name":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effective Snippet Clustering with Domain Knowledge\",\"authors\":\"S. Patro, Wei Wang\",\"doi\":\"10.1109/DBKDA.2009.8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering Web search result is a promising way to help alleviate the information overload for Web users. In this paper, we focus on clustering snippets returned by Google Scholar. We propose a novel similarity function based on mining domain knowledge and an outlier-conscious clustering algorithm. Experimental results showed improved effectiveness of the proposed approach compared with existing methods.\",\"PeriodicalId\":231150,\"journal\":{\"name\":\"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DBKDA.2009.8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DBKDA.2009.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

聚类Web搜索结果是一种很有前途的方法，可以帮助减轻Web用户的信息过载。在本文中，我们主要关注谷歌Scholar返回的聚类片段。我们提出了一种新的基于挖掘领域知识的相似度函数和一种异常值意识聚类算法。实验结果表明，与现有方法相比，该方法的有效性得到了提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Effective Snippet Clustering with Domain Knowledge

Clustering Web search result is a promising way to help alleviate the information overload for Web users. In this paper, we focus on clustering snippets returned by Google Scholar. We propose a novel similarity function based on mining domain knowledge and an outlier-conscious clustering algorithm. Experimental results showed improved effectiveness of the proposed approach compared with existing methods.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2009 First International Confernce on Advances in Databases, Knowledge, and Data Applications

自引率

0.00%

发文量