A Fast K-prototypes Algorithm Using Partial Distance Computation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Byoungwook | - |
dc.date.accessioned | 2021-09-03T07:56:10Z | - |
dc.date.available | 2021-09-03T07:56:10Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-04 | - |
dc.identifier.issn | 2073-8994 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/84005 | - |
dc.description.abstract | The k-means is one of the most popular and widely used clustering algorithm; however, it is limited to numerical data only. The k-prototypes algorithm is an algorithm famous for dealing with both numerical and categorical data. However, there have been no studies to accelerate it. In this paper, we propose a new, fast k-prototypes algorithm that provides the same answers as those of the original k-prototypes algorithm. The proposed algorithm avoids distance computations using partial distance computation. Our k-prototypes algorithm finds minimum distance without distance computations of all attributes between an object and a cluster center, which allows it to reduce time complexity. A partial distance computation uses a fact that a value of the maximum difference between two categorical attributes is 1 during distance computations. If data objects havem categorical attributes, the maximum difference of categorical attributes between an object and a cluster center is m. Our algorithm first computes distance with numerical attributes only. If a difference of the minimum distance and the second smallest with numerical attributes is higher than m, we can find the minimum distance between an object and a cluster center without distance computations of categorical attributes. The experimental results show that the computational performance of the proposed k-prototypes algorithm is superior to the original k-prototypes algorithm in our dataset. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI AG | - |
dc.subject | VECTOR QUANTIZATION | - |
dc.title | A Fast K-prototypes Algorithm Using Partial Distance Computation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Byoungwook | - |
dc.identifier.doi | 10.3390/sym9040058 | - |
dc.identifier.scopusid | 2-s2.0-85018701516 | - |
dc.identifier.wosid | 000401810100012 | - |
dc.identifier.bibliographicCitation | SYMMETRY-BASEL, v.9, no.4 | - |
dc.relation.isPartOf | SYMMETRY-BASEL | - |
dc.citation.title | SYMMETRY-BASEL | - |
dc.citation.volume | 9 | - |
dc.citation.number | 4 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | VECTOR QUANTIZATION | - |
dc.subject.keywordAuthor | clustering algorithm | - |
dc.subject.keywordAuthor | k-prototypes algorithm | - |
dc.subject.keywordAuthor | partial distance computation | - |
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