An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects
Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient...
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Veröffentlicht in: | Sustainability 2018-07, Vol.10 (8), p.2614 |
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description | Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. Experimental results show that the proposed algorithms can achieve better performance than the existing pruning techniques of the k-prototypes algorithm. |
doi_str_mv | 10.3390/su10082614 |
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Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. 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Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. Experimental results show that the proposed algorithms can achieve better performance than the existing pruning techniques of the k-prototypes algorithm.</description><subject>Algorithms</subject><subject>Clustering</subject><subject>Data processing</subject><subject>Decision making</subject><subject>Dependence</subject><subject>Prototypes</subject><subject>Pruning</subject><subject>Spatial data</subject><subject>Sustainability</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNkE1LAzEYhIMoWGov_oKANyGaj93s7rHWtoqVCtXzks1HTd0ma5I99N-7pYLOZeYwzPvyAHBN8B1jFb6PPcG4pJxkZ2BEcUEQwTk-_5cvwSTGHR7EGKkIHwExdXBujJVWuwSXwSr0IKJW8AW9BZ98OnQ6wmm79cGmzz00PsBNH5OwTjStho9a2mi9Q6_iy7ot9A5uOpGsaOG62WmZ4hW4MKKNevLrY_CxmL_PntBqvXyeTVdI0ipPSHOqjKyMwjLTRUlzJSsqidKYNBmnpSgabcosp1leNkYN31PFGkwY5liXnLMxuDntdsF_9zqmeuf74IaTNSUDmLwg1bF1e2rJ4GMM2tRdsHsRDjXB9ZFi_UeR_QCHIWPC</recordid><startdate>20180725</startdate><enddate>20180725</enddate><creator>Jang, Hong-Jun</creator><creator>Kim, Byoungwook</creator><creator>Kim, Jongwan</creator><creator>Jung, Soon-Young</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20180725</creationdate><title>An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects</title><author>Jang, Hong-Jun ; Kim, Byoungwook ; Kim, Jongwan ; Jung, Soon-Young</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-e62dfc9fd0c4e7825dc92c1de01b4628a7bef8452458bfd1912d3b013060e8663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Clustering</topic><topic>Data processing</topic><topic>Decision making</topic><topic>Dependence</topic><topic>Prototypes</topic><topic>Pruning</topic><topic>Spatial data</topic><topic>Sustainability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jang, Hong-Jun</creatorcontrib><creatorcontrib>Kim, Byoungwook</creatorcontrib><creatorcontrib>Kim, Jongwan</creatorcontrib><creatorcontrib>Jung, Soon-Young</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jang, Hong-Jun</au><au>Kim, Byoungwook</au><au>Kim, Jongwan</au><au>Jung, Soon-Young</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects</atitle><jtitle>Sustainability</jtitle><date>2018-07-25</date><risdate>2018</risdate><volume>10</volume><issue>8</issue><spage>2614</spage><pages>2614-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. 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subjects | Algorithms Clustering Data processing Decision making Dependence Prototypes Pruning Spatial data Sustainability |
title | An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects |
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