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Conference Paper: Reducing UK-means to k-means
Title | Reducing UK-means to k-means |
---|---|
Authors | |
Keywords | Administrative data processing Algorithms Boolean functions Chlorine compounds Clustering algorithms |
Issue Date | 2007 |
Publisher | IEEE, Computer Society. |
Citation | The 7th IEEE International Conference on Data Mining (ICDM) Workshops 2007, Omaha, NE., 28-31 October 2007. In Proceedings of the 7th ICDM, 2007, p. 483-488 How to Cite? |
Abstract | This paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to handle objects whose locations are uncertain. The location of each object is described by a probability density function (pdf). The UK-means algorithm needs to compute expected distances (EDs) between each object and the cluster representatives. The evaluation of ED from first principles is very costly operation, because the pdf's are different and arbitrary. But UK-means needs to evaluate a lot of EDs. This is a major performance burden of the algorithm. In this paper, we derive a formula for evaluating EDs efficiently. This tremendously reduces the execution time of UK-means, as demonstrated by our preliminary experiments. We also illustrate that this optimised formula effectively reduces the UK-means problem to the traditional clustering algorithm addressed by the k-means algorithm. © 2007 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/129555 |
ISSN | 2020 SCImago Journal Rankings: 0.545 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, SD | en_HK |
dc.contributor.author | Kao, B | en_HK |
dc.contributor.author | Cheng, R | en_HK |
dc.date.accessioned | 2010-12-23T08:39:15Z | - |
dc.date.available | 2010-12-23T08:39:15Z | - |
dc.date.issued | 2007 | en_HK |
dc.identifier.citation | The 7th IEEE International Conference on Data Mining (ICDM) Workshops 2007, Omaha, NE., 28-31 October 2007. In Proceedings of the 7th ICDM, 2007, p. 483-488 | en_HK |
dc.identifier.issn | 1550-4786 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/129555 | - |
dc.description.abstract | This paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to handle objects whose locations are uncertain. The location of each object is described by a probability density function (pdf). The UK-means algorithm needs to compute expected distances (EDs) between each object and the cluster representatives. The evaluation of ED from first principles is very costly operation, because the pdf's are different and arbitrary. But UK-means needs to evaluate a lot of EDs. This is a major performance burden of the algorithm. In this paper, we derive a formula for evaluating EDs efficiently. This tremendously reduces the execution time of UK-means, as demonstrated by our preliminary experiments. We also illustrate that this optimised formula effectively reduces the UK-means problem to the traditional clustering algorithm addressed by the k-means algorithm. © 2007 IEEE. | en_HK |
dc.language | eng | en_US |
dc.publisher | IEEE, Computer Society. | - |
dc.relation.ispartof | Proceedings of the IEEE International Conference on Data Mining, ICDM 2007 | en_HK |
dc.rights | ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
dc.subject | Administrative data processing | - |
dc.subject | Algorithms | - |
dc.subject | Boolean functions | - |
dc.subject | Chlorine compounds | - |
dc.subject | Clustering algorithms | - |
dc.title | Reducing UK-means to k-means | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Kao, B:kao@cs.hku.hk | en_HK |
dc.identifier.email | Cheng, R:ckcheng@cs.hku.hk | en_HK |
dc.identifier.authority | Kao, B=rp00123 | en_HK |
dc.identifier.authority | Cheng, R=rp00074 | en_HK |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/ICDMW.2007.40 | en_HK |
dc.identifier.scopus | eid_2-s2.0-49549099954 | en_HK |
dc.identifier.hkuros | 176467 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-49549099954&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 483 | en_HK |
dc.identifier.epage | 488 | en_HK |
dc.description.other | The 7th IEEE International Conference on Data Mining (ICDM) Workshops 2007, Omaha, NE., 28-31 October 2007. In Proceedings of the 7th ICDM, 2007, p. 483-488 | - |
dc.identifier.scopusauthorid | Lee, SD=51964193400 | en_HK |
dc.identifier.scopusauthorid | Kao, B=35221592600 | en_HK |
dc.identifier.scopusauthorid | Cheng, R=7201955416 | en_HK |
dc.identifier.issnl | 1550-4786 | - |