A GPU-accelerated density-based clustering algorithm
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Loh, W.-K. | - |
dc.contributor.author | Kim, Y.-K. | - |
dc.date.available | 2020-02-28T18:45:36Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/13050 | - |
dc.description.abstract | Due to the advances in GPU technology, there have been many approaches to utilize the GPU for general applications. Many research papers that dramatically improved the performance of traditional CPU-based data mining algorithms have been published. Clustering is an important data mining problem that is often found in many areas. DBSCAN is the most widely used density-based clustering algorithm, but it has a drawback that the optimal parameters can be hardly found. OPTICS was proposed to tackle the problem. In this paper, we propose an algorithm that significantly improves the performance of OPTICS using the GPU. Through extensive experiments, we show that our algorithm outperforms OPTICS by an order of magnitude. © 2014 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014 | - |
dc.subject | Big data | - |
dc.subject | Data mining | - |
dc.subject | Graphics processing unit | - |
dc.subject | Data mining algorithm | - |
dc.subject | Data mining problems | - |
dc.subject | Density-based clustering algorithms | - |
dc.subject | Divide and conquer | - |
dc.subject | General applications | - |
dc.subject | GPU-accelerated | - |
dc.subject | Optimal parameter | - |
dc.subject | Research papers | - |
dc.subject | Clustering algorithms | - |
dc.title | A GPU-accelerated density-based clustering algorithm | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000380451500115 | - |
dc.identifier.doi | 10.1109/BDCloud.2014.130 | - |
dc.identifier.bibliographicCitation | Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, pp.775 - 776 | - |
dc.identifier.scopusid | 2-s2.0-84924370748 | - |
dc.citation.endPage | 776 | - |
dc.citation.startPage | 775 | - |
dc.citation.title | Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014 | - |
dc.contributor.affiliatedAuthor | Loh, W.-K. | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | density-based clustering | - |
dc.subject.keywordAuthor | gpu | - |
dc.subject.keywordAuthor | parallel algorithm | - |
dc.subject.keywordAuthor | cuda | - |
dc.subject.keywordAuthor | divide-and-conquer | - |
dc.subject.keywordPlus | Big data | - |
dc.subject.keywordPlus | Data mining | - |
dc.subject.keywordPlus | Graphics processing unit | - |
dc.subject.keywordPlus | Data mining algorithm | - |
dc.subject.keywordPlus | Data mining problems | - |
dc.subject.keywordPlus | Density-based clustering algorithms | - |
dc.subject.keywordPlus | Divide and conquer | - |
dc.subject.keywordPlus | General applications | - |
dc.subject.keywordPlus | GPU-accelerated | - |
dc.subject.keywordPlus | Optimal parameter | - |
dc.subject.keywordPlus | Research papers | - |
dc.subject.keywordPlus | Clustering algorithms | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scopus | - |
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