Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD
In this paper we describe a way to discover attribute associations and a way to present them to users using Bayesian networks. We describe a three-dimensional visualization to present them effectively to users. Furthermore we discuss two applications of attribute associations to the KDD process. One...
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| creator | Masuda, Gou Yano, Rei Sakamoto, Norihiro Ushijima, Kazuo |
| description | In this paper we describe a way to discover attribute associations and a way to present them to users using Bayesian networks. We describe a three-dimensional visualization to present them effectively to users. Furthermore we discuss two applications of attribute associations to the KDD process. One application involves using them to support feature selection. The result of our experiment shows that feature selection using visualized attribute associations works well in 17 data sets out of the 24 that were used. The other application uses them to support the selection of data mining methods. We discuss the possibility of using attribute associations to help in deciding if a given data set is suited to learning decision trees. We found 3 types of structural characteristics in Bayesian networks obtained from the data. The characteristics have strong relevance to the results of learning decision trees. |
| doi_str_mv | 10.1007/978-3-540-48247-5_7 |
| format | Book Chapter |
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Data processing</topic><topic>Software</topic><topic>Star Type</topic><topic>Target Concept</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Masuda, Gou</creatorcontrib><creatorcontrib>Yano, Rei</creatorcontrib><creatorcontrib>Sakamoto, Norihiro</creatorcontrib><creatorcontrib>Ushijima, Kazuo</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Masuda, Gou</au><au>Yano, Rei</au><au>Sakamoto, Norihiro</au><au>Ushijima, Kazuo</au><au>Zytkow, Jan</au><au>Rauch, Jan</au><au>van Leeuwen, Jan</au><au>Żytkow, Jan M.</au><au>Rauch, Jan</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD</atitle><btitle>Lecture notes in computer science</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>1999</date><risdate>1999</risdate><volume>1704</volume><spage>61</spage><epage>70</epage><pages>61-70</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540664904</isbn><isbn>9783540664901</isbn><eisbn>3540482474</eisbn><eisbn>9783540482475</eisbn><abstract>In this paper we describe a way to discover attribute associations and a way to present them to users using Bayesian networks. 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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 1999, Vol.1704, p.61-70 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_1829336 |
| source | Springer Books |
| subjects | Applied sciences Bayesian Network Computer science control theory systems Data Mining Method Exact sciences and technology Feature Selection Information systems. Data bases Memory organisation. Data processing Software Star Type Target Concept |
| title | Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD |
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