Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105448
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Liu, H | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Zhang, X | en_US |
dc.creator | Li, Q | en_US |
dc.creator | Wu, XM | en_US |
dc.date.accessioned | 2024-04-15T07:34:26Z | - |
dc.date.available | 2024-04-15T07:34:26Z | - |
dc.identifier.issn | 0140-3664 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105448 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.rights | © 2021 Elsevier B.V. All rights reserved. | en_US |
dc.rights | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Liu, H., Zhang, X., Zhang, X., Li, Q., & Wu, X. M. (2021). RPC: Representative possible world based consistent clustering algorithm for uncertain data. Computer Communications, 176, 128-137 is available at https://doi.org/10.1016/j.comcom.2021.06.002. | en_US |
dc.subject | Clustering | en_US |
dc.subject | Consistency learning | en_US |
dc.subject | Possible world | en_US |
dc.subject | Uncertain data | en_US |
dc.title | RPC : representative possible world based consistent clustering algorithm for uncertain data | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 128 | en_US |
dc.identifier.epage | 137 | en_US |
dc.identifier.volume | 176 | en_US |
dc.identifier.doi | 10.1016/j.comcom.2021.06.002 | en_US |
dcterms.abstract | Clustering uncertain data is an essential task in data mining and machine learning. Possible world based algorithms seem promising for clustering uncertain data. However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects. (2) They do not well utilize the consistency among possible worlds, since they conduct clustering or construct the affinity matrix on each possible world independently. In this paper, we propose a representative possible world based consistent clustering (RPC) algorithm for uncertain data. First, by introducing representative loss and using Jensen–Shannon divergence as the distribution measure, we design a heuristic strategy for the selection of representative possible worlds, thus avoiding the negative effects caused by marginal possible worlds. Second, we integrate a consistency learning procedure into spectral clustering to deal with the representative possible worlds synergistically, thus utilizing the consistency to achieve better performance. Experimental results show that our proposed algorithm outperforms existing algorithms in effectiveness and performs competitively in efficiency. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Computer communications, 1 Aug. 2021, v. 176, p. 128-137 | en_US |
dcterms.isPartOf | Computer communications | en_US |
dcterms.issued | 2021-08-01 | - |
dc.identifier.scopus | 2-s2.0-85107549826 | - |
dc.identifier.eissn | 1873-703X | en_US |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0016 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 53563325 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Rpc_Representative_Possible.pdf | Pre-Published version | 1.57 MB | Adobe PDF | View/Open |
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