A multi-agent-based algorithm for data clustering
Clustering algorithms aim to detect groups based on similarity, from a given set of objects. Many clustering techniques have been proposed, most requiring the user to set critical parameters, such as the number of groups. This work presents the implementation and evaluation of a clustering algorithm...
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| Veröffentlicht in: | Progress in artificial intelligence 2020-12, Vol.9 (4), p.305-313 |
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| creator | Godois, Lutiele M. Adamatti, Diana F. Emmendorfer, Leonardo R. |
| description | Clustering algorithms aim to detect groups based on similarity, from a given set of objects. Many clustering techniques have been proposed, most requiring the user to set critical parameters, such as the number of groups. This work presents the implementation and evaluation of a clustering algorithm based on a multi-agent system, which automatically detects the number of groups and the group labels for a given dataset. Groups formed during the clustering process emerge as patterns from the interaction among agents. The proposed algorithm is experimentally validated over benchmark datasets from the literature. The quality of clustering results is computed using seven internal indexes and one external index. Under this methodology, the proposed algorithm is compared to K-means and DBSCAN (density-based spatial clustering of applications with noise). |
| doi_str_mv | 10.1007/s13748-020-00213-3 |
| format | Article |
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Under this methodology, the proposed algorithm is compared to K-means and DBSCAN (density-based spatial clustering of applications with noise).</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Clustering</subject><subject>Computational Intelligence</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Control</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Datasets</subject><subject>Mechatronics</subject><subject>Multiagent systems</subject><subject>Natural Language Processing (NLP)</subject><subject>Object recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Regular Paper</subject><subject>Robotics</subject><subject>Vision</subject><issn>2192-6352</issn><issn>2192-6360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EElXpH2CKxGy4sx07GauKL6kSC8yW49ghVdIU2xn49yQEwcZ0N7zPfTyEXCPcIoC6i8iVKCgwoAAMOeVnZMWwZFRyCee_fc4uySbGA8wpAcjFiuA268cutdQ07phoZaKrM9M1Q2jTe5_5IWS1SSaz3RiTC-2xuSIX3nTRbX7qmrw93L_unuj-5fF5t91Ty7FMVDhAx72vgGPuQRY2d9JArnwpZaUsV-V0g3cOgFtlCysQrCyMr0vBKmf5mtwsc09h-BhdTPowjOE4rdRMqAkHRD6l2JKyYYgxOK9Poe1N-NQIerajFzt6sqO_7egZ4gsUT_NHLvyN_of6AuwHZb0</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Godois, Lutiele M.</creator><creator>Adamatti, Diana F.</creator><creator>Emmendorfer, Leonardo R.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-3829-3075</orcidid></search><sort><creationdate>20201201</creationdate><title>A multi-agent-based algorithm for data clustering</title><author>Godois, Lutiele M. ; Adamatti, Diana F. ; Emmendorfer, Leonardo R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-4e01e3ffb0315f068c5e6a057f966b7c379140fee003c7c8c410c68afd942bec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Clustering</topic><topic>Computational Intelligence</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Control</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Datasets</topic><topic>Mechatronics</topic><topic>Multiagent systems</topic><topic>Natural Language Processing (NLP)</topic><topic>Object recognition</topic><topic>Pattern Recognition and Graphics</topic><topic>Regular Paper</topic><topic>Robotics</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Godois, Lutiele M.</creatorcontrib><creatorcontrib>Adamatti, Diana F.</creatorcontrib><creatorcontrib>Emmendorfer, Leonardo R.</creatorcontrib><collection>CrossRef</collection><jtitle>Progress in artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Godois, Lutiele M.</au><au>Adamatti, Diana F.</au><au>Emmendorfer, Leonardo R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multi-agent-based algorithm for data clustering</atitle><jtitle>Progress in artificial intelligence</jtitle><stitle>Prog Artif Intell</stitle><date>2020-12-01</date><risdate>2020</risdate><volume>9</volume><issue>4</issue><spage>305</spage><epage>313</epage><pages>305-313</pages><issn>2192-6352</issn><eissn>2192-6360</eissn><abstract>Clustering algorithms aim to detect groups based on similarity, from a given set of objects. 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| subjects | Algorithms Artificial Intelligence Clustering Computational Intelligence Computer Imaging Computer Science Control Data Mining and Knowledge Discovery Datasets Mechatronics Multiagent systems Natural Language Processing (NLP) Object recognition Pattern Recognition and Graphics Regular Paper Robotics Vision |
| title | A multi-agent-based algorithm for data clustering |
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