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Conference Paper: Mining order-preserving submatrices from data with repeated measurements
Title | Mining order-preserving submatrices from data with repeated measurements |
---|---|
Authors | |
Keywords | Absolute values Computational challenges Concurrent pattern Data items Data noise |
Issue Date | 2008 |
Publisher | IEEE Computer Society. |
Citation | The 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 15-19 December 2008. In Data Mining ICDM 2008 Proceedings, 2008, p. 133-142 How to Cite? |
Abstract | Order-preserving submatrices (OPSM's) have been shown useful in capturing concurrent patterns in data when the relative magnitudes of data items are more important than their absolute values. To cope with data noise, repeated experiments are often conducted to collect multiple measurements. We propose and study a more robust version of OPSM, where each data item is represented by a set of values obtained from replicated experiments. We call the new problem OPSM-RM (OPSM with repeated measurements). We define OPSM-RM based on a number of practical requirements. We discuss the computational challenges of OPSM-RM and propose a generic mining algorithm. We further propose a series of techniques to speed up two timedominating components of the algorithm. We clearly show the effectiveness of our methods through a series of experiments conducted on real microarray data. |
Persistent Identifier | http://hdl.handle.net/10722/61197 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.545 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chui, CK | en_HK |
dc.contributor.author | Kao, B | en_HK |
dc.contributor.author | Yip, KYL | en_HK |
dc.contributor.author | Lee, SD | en_HK |
dc.date.accessioned | 2010-07-13T03:32:58Z | - |
dc.date.available | 2010-07-13T03:32:58Z | - |
dc.date.issued | 2008 | en_HK |
dc.identifier.citation | The 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 15-19 December 2008. In Data Mining ICDM 2008 Proceedings, 2008, p. 133-142 | en_HK |
dc.identifier.isbn | 9780769535029 | - |
dc.identifier.issn | 1550-4786 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/61197 | - |
dc.description.abstract | Order-preserving submatrices (OPSM's) have been shown useful in capturing concurrent patterns in data when the relative magnitudes of data items are more important than their absolute values. To cope with data noise, repeated experiments are often conducted to collect multiple measurements. We propose and study a more robust version of OPSM, where each data item is represented by a set of values obtained from replicated experiments. We call the new problem OPSM-RM (OPSM with repeated measurements). We define OPSM-RM based on a number of practical requirements. We discuss the computational challenges of OPSM-RM and propose a generic mining algorithm. We further propose a series of techniques to speed up two timedominating components of the algorithm. We clearly show the effectiveness of our methods through a series of experiments conducted on real microarray data. | en_HK |
dc.language | eng | en_HK |
dc.publisher | IEEE Computer Society. | - |
dc.relation.ispartof | Data Mining ICDM 2008 Proceedings | en_HK |
dc.rights | ©2008 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 | Absolute values | - |
dc.subject | Computational challenges | - |
dc.subject | Concurrent pattern | - |
dc.subject | Data items | - |
dc.subject | Data noise | - |
dc.title | Mining order-preserving submatrices from data with repeated measurements | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Chui, CK: ckchui@cs.hku.hk | en_HK |
dc.identifier.email | Kao, B: kao@cs.hku.hk | - |
dc.identifier.email | Yip, KYL: yuklap.yip@yale.edu | - |
dc.identifier.email | Lee, SD: sdlee@cs.hku.hk | - |
dc.identifier.authority | Kao, B=rp00123 | en_HK |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/ICDM.2008.12 | en_HK |
dc.identifier.scopus | eid_2-s2.0-67049100145 | en_HK |
dc.identifier.hkuros | 200232 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-67049100145&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 133 | en_HK |
dc.identifier.epage | 142 | en_HK |
dc.publisher.place | United States | - |
dc.description.other | The 8th IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy, 15-19 December 2008. In Data Mining ICDM 2008 Proceedings, 2008, p. 133-142 | - |
dc.identifier.scopusauthorid | Lee, SD=7601400741 | en_HK |
dc.identifier.scopusauthorid | Yip, KY=34574226200 | en_HK |
dc.identifier.scopusauthorid | Kao, B=35221592600 | en_HK |
dc.identifier.scopusauthorid | Chui, CK=21741958100 | en_HK |
dc.identifier.issnl | 1550-4786 | - |