Adaptive evolutionary clustering
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term t...
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| Published in | Data mining and knowledge discovery Vol. 28; no. 2; pp. 304 - 336 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.03.2014
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1384-5810 1573-756X |
| DOI | 10.1007/s10618-012-0302-x |
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| Abstract | In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios. |
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| AbstractList | In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios. In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios. In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naïve estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.[PUBLICATION ABSTRACT] |
| Author | Xu, Kevin S. Kliger, Mark Hero III, Alfred O. |
| Author_xml | – sequence: 1 givenname: Kevin S. surname: Xu fullname: Xu, Kevin S. email: xukevin@umich.edu organization: EECS Department, University of Michigan – sequence: 2 givenname: Mark surname: Kliger fullname: Kliger, Mark organization: Omek Interactive – sequence: 3 givenname: Alfred O. surname: Hero III fullname: Hero III, Alfred O. organization: EECS Department, University of Michigan |
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| Cites_doi | 10.1145/1835804.1835880 10.2139/ssrn.1787577 10.1137/1.9781611972788.20 10.1002/nav.3800020109 10.1145/1148170.1148241 10.1080/01621459.1971.10482356 10.1145/1281192.1281269 10.1109/TSP.2010.2053029 10.1145/37401.37406 10.1109/ICASSP.2010.5495655 10.1145/1150402.1150467 10.1007/BF02294245 10.1137/1.9781611972740.14 10.1109/ICDMW.2008.93 10.1002/0471221546 10.1109/ASONAM.2010.17 10.1145/1281192.1281266 10.1145/1401890.1401972 10.1016/S0927-5398(03)00007-0 10.1142/S0219720009004114 10.1007/s13278-012-0058-8 10.1073/pnas.0601602103 10.1016/0377-0427(87)90125-7 10.1016/j.patcog.2009.06.001 10.1073/pnas.0900282106 10.2202/1544-6115.1175 10.1109/WI.2006.118 10.1126/science.1184819 10.1007/s11222-007-9033-z 10.1145/1835804.1835877 10.1007/978-0-387-21606-5 10.1109/34.868688 10.1145/1835804.1835940 10.1145/1014052.1014129 10.1137/S0097539702418498 10.1007/s10994-010-5214-7 10.1137/1.9781611972771.12 10.1093/bioinformatics/btl242 |
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| Keywords | Evolutionary clustering Tracking Similarity measures Clustering algorithms Adaptive filtering Shrinkage estimation Data smoothing |
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| References | HastieTTibshiraniRFriedmanJThe elements of statistical learning: data mining, inference, and prediction2001New YorkSpringer10.1007/978-0-387-21606-5 Reynolds CW (1987) Flocks, herds, and schools: A distributed behavioral model. Proceedings of 14th annual conference on computer graphics and interactive techniques, Anaheim HaykinSKalman filtering and neural networks2001New YorkWiley-Interscience10.1002/0471221546 MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley symposium on mathematical statistics and probability Carmi A, Septier F, Godsill SJ (2009) The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. Proceedings of the 12th international conference on information fusion, Seattle LütkepohlHHandbook of matrices1997New YorkWiley Infochimps-WWW (2012) NASDAQ Exchange Daily 1970–2010 Open, Close, High, Low and Volume data set. http://www.infochimps.com/datasets/nasdaq-exchange-daily-1970-2010-open-close-high-low-and-volume von LuxburgUA tutorial on spectral clusteringStat Comput200717439541610.1007/s11222-007-9033-z2409803 RousseeuwPJSilhouettes: a graphical aid to the interpretation and validation of cluster analysisJ Computat Appl Math1987205365 MIT-WWW (2005) MIT academic calendar 2004–2005. http://web.mit.edu/registrar/www/calendar0405.html EagleNPentlandALazerDInferring friendship network structure by using mobile phone dataProc Nat Acad Sci200910636152741527810.1073/pnas.0900282106 CharikarMChekuriCFederTMotwaniRIncremental clustering and dynamic information retrievalSIAM J Comput20043361417144010.1137/S00975397024184981101.686052112724 KuhnHWThe Hungarian method for the assignment problemNav Res Logist Quart195521–2839710.1002/nav.3800020109 LinYRChiYZhuSSundaramHTsengBLAnalyzing communities and their evolutions in dynamic social networksACM Trans Knowl Discov Data200932810.1145/1514888.1514891 Wang Y, Liu SX, Feng J, Zhou L (2007) Mining naturally smooth evolution of clusters from dynamic data. Proceedings of SIAM conference on data mining MuchaPJRichardsonTMaconKPorterMAOnnelaJPCommunity structure in time-dependent, multiscale, and multiplex networksScience2010328598087687810.1126/science.11848191226.910562662590 Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, Philadelphia NingHXuWChiYGongYHuangTSIncremental spectral clustering by efficiently updating the eigen-systemPattern Recog201043111312710.1016/j.patcog.2009.06.0011176.68186 Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained K-means clustering with background knowledge. Proceedings of the 18th international conference on machine learning, pp 577–584 Wang X, Davidson I (2010) Flexible constrained spectral clustering. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 563–572 Tantipathananandh C, Berger-Wolf T, Kempe D (2007) A framework for community identification in dynamic social networks. Proceedings of 13th ACM SIGKDD international conference on knowledge discovery and data mining Gretton A, Borgwardt KM, Rasch M, Schölkopf B, Smola AJ (2007) A kernel approach to comparing distributions. Proceedings of the 22nd AAAI conference on artificial intelligence HarveyACForecasting, structural time series models and the Kalman filter1989CambridgeCambridge University Press Bródka P, Saganowski S, Kazienko P (2012) GED: the method for group evolution discovery in social networks. Soc Netw Anal Min. doi: 10.1007/s13278-012-0058-8 Gupta C, Grossman R (2004) GenIc: a single pass generalized incremental algorithm for clustering. Proceedings SIAM conference on data mining, Lake Buena Vista ChiYSongXZhouDHinoKTsengBLOn evolutionary spectral clusteringACM Trans Knowl Discov Data2009341710.1145/1631162.1631165 Xu KS, Kliger M, Hero AO III (2010) Evolutionary spectral clustering with adaptive forgetting factor. Proceeding of IEEE international conference on acoustics, speech, and signal processing RandWMObjective criteria for the evaluation of clustering methodsJ Am Stat Assoc197166336846850 AndersonTWAn introduction to multivariate statistical analysis20033HobokenWiley1039.62044 NASDAQ-WWW (2012) NASDAQ Companies. http://www.nasdaq.com/screening/companies-by-industry.aspx?exchange=NASDAQ Rosswog J, Ghose K (2008) Detecting and tracking spatio-temporal clusters with adaptive history filtering. Proceedings of the 8th IEEE international conference on data mining workshops, Pisa Parker C (2007) Boids pseudocode. http://www.vergenet.net/conrad/boids/pseudocode.html YangTChiYZhuSGongYJinRDetecting communities and their evolutions in dynamic social networks—a Bayesian approachMach Learn201182215718910.1007/s10994-010-5214-71237.911893108191 Zhang J, Song Y, Chen G, Zhang C (2009) On-line evolutionary exponential family mixture. Proceedings of the 21st international joint conference on artificial intelligence, Pasadena ShiJMalikJNormalized cuts and image segmentationIEEE Trans Pattern Anal Mach Intell200022888890510.1109/34.868688 Tang L, Liu H, Zhang J, Nazeri Z (2008) Community evolution in dynamic multi-mode networks. Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining Gavrilov M, Anguelov D, Indyk P, Motwani R (2000) Mining the stock market: Which measure is best? Proceedings of 6th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, pp 487–496 NgAYJordanMIWeissYOn spectral clustering: analysis and an algorithmAdv Neural Inf Process Syst200114849856 Xu T, Zhang Z, Yu PS, Long B (2008a) Dirichlet process based evolutionary clustering. Proceedings of the 8th IEEE international conference on data mining Ahmed A, Xing EP (2008) Dynamic non-parametric mixture models and the recurrent chinese restaurant process: with applications to evolutionary clustering. Proceedings of the SIAM international conference on data mining, Atlanta FennDJPorterMAMcDonaldMWilliamsSJohnsonNFJonesNSDynamic communities in multichannel data: an application to the foreign exchange market during the 2007–2008 credit crisisChaos200919033119 NewmanMEJModularity and community structure in networksProc Nat Acad Sci20061032385778582 Yahoo-WWW (2012) IXIC Historical Prices|NASDAQ composite stock—Yahoo! Finance. http://finance.yahoo.com/q/hp?s=IXIC+Historical+Prices Zhang J, Song Y, Zhang C, Liu S (2010) Evolutionary hierarchical Dirichlet processes for multiple correlated time-varying corpora. Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining LedoitOWolfMImproved estimation of the covariance matrix of stock returns with an application to portfolio selectionJ Empir Financ200310560362110.1016/S0927-5398(03)00007-0 MilliganGWCooperMCAn examination of procedures for determining the number of clusters in a data setPsychometrika198550215917910.1007/BF02294245 Ji X, Xu W (2006) Document clustering with prior knowledge. Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, New York, pp 405–412 SchäferJStrimmerKA shrinkage approach to large-scale covariance matrix estimation and implications for functional genomicsStat Appl Genet Mol Biol200541322183942 Sun J, Papadimitriou S, Yu PS, Faloutsos C (2007) Graphscope: Parameter-free mining of large time-evolving graphs. Proceedings of 13th ACM SIGKDD conference on knowledge discovery and data mining Falkowski T, Bartelheimer J, Spiliopoulou M (2006) Mining and visualizing the evolution of subgroups in social networks. Proceedings of the IEEE/WIC/ACM international conference on web intelligence, Hong Kong Chung FRK (1997) Spectral graph theory. American Mathematical Society, Providence ChenYWieselAEldarYCShrinkage algorithms for MMSE covariance estimationIEEE Trans Signal Process201058105016502910.1109/TSP.2010.20530292722661 Li Y, Han J, Yang J (2004) Clustering moving objects. Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining Hossain MS, Tadepalli S, Watson LT, Davidson I, Helm RF, Ramakrishnan N (2010) Unifying dependent clustering and disparate clustering for non-homogeneous data. Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 593–602 Mankad S, Michailidis G, Kirilenko A (2011) Smooth plaid models: a dynamic clustering algorithm with application to electronic financial markets. Tech Rep. http://ssrn.com/abstract=1787577 Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. Proceedings of international conference on advanced social network analysis and mining, pp 176–183 Xu T, Zhang Z, Yu PS, Long B (2008b) Evolutionary clustering by hierarchical Dirichlet process with hidden Markov state. Proceedings of the 8th IEEE international conference on data mining TadepalliSRamakrishnanNWatsonLTMishraBHelmRFGene expression time courses by analyzing cluster dynamicsJ Bioinforma Comput Biol20097233935610.1142/S0219720009004114 PJ Mucha (302_CR32) 2010; 328 302_CR41 302_CR40 302_CR46 H Lütkepohl (302_CR27) 1997 302_CR44 AC Harvey (302_CR17) 1989 302_CR49 302_CR47 GW Milligan (302_CR30) 1985; 50 TW Anderson (302_CR2) 2003 HW Kuhn (302_CR23) 1955; 2 S Tadepalli (302_CR45) 2009; 7 YR Lin (302_CR26) 2009; 3 O Ledoit (302_CR24) 2003; 10 302_CR31 302_CR35 302_CR34 T Hastie (302_CR18) 2001 302_CR33 DJ Fenn (302_CR12) 2009; 19 302_CR39 302_CR38 302_CR37 302_CR29 M Charikar (302_CR6) 2004; 33 Y Chen (302_CR7) 2010; 58 N Eagle (302_CR10) 2009; 106 302_CR20 S Haykin (302_CR19) 2001 302_CR22 302_CR21 T Yang (302_CR56) 2011; 82 302_CR28 302_CR25 U Luxburg von (302_CR48) 2007; 17 302_CR9 J Shi (302_CR43) 2000; 22 Y Chi (302_CR8) 2009; 3 302_CR53 302_CR52 302_CR51 302_CR50 302_CR4 302_CR13 302_CR57 302_CR3 302_CR11 H Ning (302_CR36) 2010; 43 302_CR55 302_CR1 302_CR54 302_CR16 302_CR15 302_CR5 302_CR14 302_CR58 J Schäfer (302_CR42) 2005; 4 |
| References_xml | – reference: Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. Proceedings of international conference on advanced social network analysis and mining, pp 176–183 – reference: Ji X, Xu W (2006) Document clustering with prior knowledge. Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, New York, pp 405–412 – reference: Mankad S, Michailidis G, Kirilenko A (2011) Smooth plaid models: a dynamic clustering algorithm with application to electronic financial markets. Tech Rep. http://ssrn.com/abstract=1787577 – reference: Gretton A, Borgwardt KM, Rasch M, Schölkopf B, Smola AJ (2007) A kernel approach to comparing distributions. Proceedings of the 22nd AAAI conference on artificial intelligence – reference: Bródka P, Saganowski S, Kazienko P (2012) GED: the method for group evolution discovery in social networks. Soc Netw Anal Min. doi: 10.1007/s13278-012-0058-8 – reference: HarveyACForecasting, structural time series models and the Kalman filter1989CambridgeCambridge University Press – reference: NingHXuWChiYGongYHuangTSIncremental spectral clustering by efficiently updating the eigen-systemPattern Recog201043111312710.1016/j.patcog.2009.06.0011176.68186 – reference: AndersonTWAn introduction to multivariate statistical analysis20033HobokenWiley1039.62044 – reference: Wang X, Davidson I (2010) Flexible constrained spectral clustering. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 563–572 – reference: NgAYJordanMIWeissYOn spectral clustering: analysis and an algorithmAdv Neural Inf Process Syst200114849856 – reference: Xu KS, Kliger M, Hero AO III (2010) Evolutionary spectral clustering with adaptive forgetting factor. Proceeding of IEEE international conference on acoustics, speech, and signal processing – reference: Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, Philadelphia – reference: RousseeuwPJSilhouettes: a graphical aid to the interpretation and validation of cluster analysisJ Computat Appl Math1987205365 – reference: HaykinSKalman filtering and neural networks2001New YorkWiley-Interscience10.1002/0471221546 – reference: Carmi A, Septier F, Godsill SJ (2009) The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. Proceedings of the 12th international conference on information fusion, Seattle – reference: ShiJMalikJNormalized cuts and image segmentationIEEE Trans Pattern Anal Mach Intell200022888890510.1109/34.868688 – reference: Parker C (2007) Boids pseudocode. http://www.vergenet.net/conrad/boids/pseudocode.html – reference: NASDAQ-WWW (2012) NASDAQ Companies. http://www.nasdaq.com/screening/companies-by-industry.aspx?exchange=NASDAQ – reference: LedoitOWolfMImproved estimation of the covariance matrix of stock returns with an application to portfolio selectionJ Empir Financ200310560362110.1016/S0927-5398(03)00007-0 – reference: Tantipathananandh C, Berger-Wolf T, Kempe D (2007) A framework for community identification in dynamic social networks. Proceedings of 13th ACM SIGKDD international conference on knowledge discovery and data mining – reference: Yahoo-WWW (2012) IXIC Historical Prices|NASDAQ composite stock—Yahoo! Finance. http://finance.yahoo.com/q/hp?s=IXIC+Historical+Prices – reference: Gavrilov M, Anguelov D, Indyk P, Motwani R (2000) Mining the stock market: Which measure is best? Proceedings of 6th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, pp 487–496 – reference: MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley symposium on mathematical statistics and probability – reference: Xu T, Zhang Z, Yu PS, Long B (2008a) Dirichlet process based evolutionary clustering. Proceedings of the 8th IEEE international conference on data mining – reference: MuchaPJRichardsonTMaconKPorterMAOnnelaJPCommunity structure in time-dependent, multiscale, and multiplex networksScience2010328598087687810.1126/science.11848191226.910562662590 – reference: Wang Y, Liu SX, Feng J, Zhou L (2007) Mining naturally smooth evolution of clusters from dynamic data. Proceedings of SIAM conference on data mining – reference: Zhang J, Song Y, Chen G, Zhang C (2009) On-line evolutionary exponential family mixture. Proceedings of the 21st international joint conference on artificial intelligence, Pasadena – reference: MIT-WWW (2005) MIT academic calendar 2004–2005. http://web.mit.edu/registrar/www/calendar0405.html – reference: Reynolds CW (1987) Flocks, herds, and schools: A distributed behavioral model. Proceedings of 14th annual conference on computer graphics and interactive techniques, Anaheim – reference: LinYRChiYZhuSSundaramHTsengBLAnalyzing communities and their evolutions in dynamic social networksACM Trans Knowl Discov Data200932810.1145/1514888.1514891 – reference: CharikarMChekuriCFederTMotwaniRIncremental clustering and dynamic information retrievalSIAM J Comput20043361417144010.1137/S00975397024184981101.686052112724 – reference: Zhang J, Song Y, Zhang C, Liu S (2010) Evolutionary hierarchical Dirichlet processes for multiple correlated time-varying corpora. Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining – reference: Hossain MS, Tadepalli S, Watson LT, Davidson I, Helm RF, Ramakrishnan N (2010) Unifying dependent clustering and disparate clustering for non-homogeneous data. Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 593–602 – reference: HastieTTibshiraniRFriedmanJThe elements of statistical learning: data mining, inference, and prediction2001New YorkSpringer10.1007/978-0-387-21606-5 – reference: SchäferJStrimmerKA shrinkage approach to large-scale covariance matrix estimation and implications for functional genomicsStat Appl Genet Mol Biol200541322183942 – reference: YangTChiYZhuSGongYJinRDetecting communities and their evolutions in dynamic social networks—a Bayesian approachMach Learn201182215718910.1007/s10994-010-5214-71237.911893108191 – reference: Falkowski T, Bartelheimer J, Spiliopoulou M (2006) Mining and visualizing the evolution of subgroups in social networks. Proceedings of the IEEE/WIC/ACM international conference on web intelligence, Hong Kong – reference: NewmanMEJModularity and community structure in networksProc Nat Acad Sci20061032385778582 – reference: von LuxburgUA tutorial on spectral clusteringStat Comput200717439541610.1007/s11222-007-9033-z2409803 – reference: ChenYWieselAEldarYCShrinkage algorithms for MMSE covariance estimationIEEE Trans Signal Process201058105016502910.1109/TSP.2010.20530292722661 – reference: Rosswog J, Ghose K (2008) Detecting and tracking spatio-temporal clusters with adaptive history filtering. Proceedings of the 8th IEEE international conference on data mining workshops, Pisa – reference: Wagstaff K, Cardie C, Rogers S, Schroedl S (2001) Constrained K-means clustering with background knowledge. Proceedings of the 18th international conference on machine learning, pp 577–584 – reference: FennDJPorterMAMcDonaldMWilliamsSJohnsonNFJonesNSDynamic communities in multichannel data: an application to the foreign exchange market during the 2007–2008 credit crisisChaos200919033119 – reference: KuhnHWThe Hungarian method for the assignment problemNav Res Logist Quart195521–2839710.1002/nav.3800020109 – reference: EagleNPentlandALazerDInferring friendship network structure by using mobile phone dataProc Nat Acad Sci200910636152741527810.1073/pnas.0900282106 – reference: MilliganGWCooperMCAn examination of procedures for determining the number of clusters in a data setPsychometrika198550215917910.1007/BF02294245 – reference: Sun J, Papadimitriou S, Yu PS, Faloutsos C (2007) Graphscope: Parameter-free mining of large time-evolving graphs. Proceedings of 13th ACM SIGKDD conference on knowledge discovery and data mining – reference: LütkepohlHHandbook of matrices1997New YorkWiley – reference: Gupta C, Grossman R (2004) GenIc: a single pass generalized incremental algorithm for clustering. Proceedings SIAM conference on data mining, Lake Buena Vista – reference: Li Y, Han J, Yang J (2004) Clustering moving objects. Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining – reference: Xu T, Zhang Z, Yu PS, Long B (2008b) Evolutionary clustering by hierarchical Dirichlet process with hidden Markov state. Proceedings of the 8th IEEE international conference on data mining – reference: RandWMObjective criteria for the evaluation of clustering methodsJ Am Stat Assoc197166336846850 – reference: Chung FRK (1997) Spectral graph theory. American Mathematical Society, Providence – reference: TadepalliSRamakrishnanNWatsonLTMishraBHelmRFGene expression time courses by analyzing cluster dynamicsJ Bioinforma Comput Biol20097233935610.1142/S0219720009004114 – reference: Ahmed A, Xing EP (2008) Dynamic non-parametric mixture models and the recurrent chinese restaurant process: with applications to evolutionary clustering. Proceedings of the SIAM international conference on data mining, Atlanta – reference: Infochimps-WWW (2012) NASDAQ Exchange Daily 1970–2010 Open, Close, High, Low and Volume data set. http://www.infochimps.com/datasets/nasdaq-exchange-daily-1970-2010-open-close-high-low-and-volume – reference: ChiYSongXZhouDHinoKTsengBLOn evolutionary spectral clusteringACM Trans Knowl Discov Data2009341710.1145/1631162.1631165 – reference: Tang L, Liu H, Zhang J, Nazeri Z (2008) Community evolution in dynamic multi-mode networks. Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining – ident: 302_CR20 doi: 10.1145/1835804.1835880 – ident: 302_CR29 doi: 10.2139/ssrn.1787577 – volume: 3 start-page: 8 issue: 2 year: 2009 ident: 302_CR26 publication-title: ACM Trans Knowl Discov Data – ident: 302_CR55 – ident: 302_CR1 doi: 10.1137/1.9781611972788.20 – ident: 302_CR4 – volume: 2 start-page: 83 issue: 1–2 year: 1955 ident: 302_CR23 publication-title: Nav Res Logist Quart doi: 10.1002/nav.3800020109 – ident: 302_CR22 doi: 10.1145/1148170.1148241 – ident: 302_CR35 – ident: 302_CR38 doi: 10.1080/01621459.1971.10482356 – ident: 302_CR47 doi: 10.1145/1281192.1281269 – volume: 58 start-page: 5016 issue: 10 year: 2010 ident: 302_CR7 publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2010.2053029 – ident: 302_CR39 doi: 10.1145/37401.37406 – ident: 302_CR31 – ident: 302_CR52 doi: 10.1109/ICASSP.2010.5495655 – ident: 302_CR5 doi: 10.1145/1150402.1150467 – volume: 50 start-page: 159 issue: 2 year: 1985 ident: 302_CR30 publication-title: Psychometrika doi: 10.1007/BF02294245 – volume: 3 start-page: 17 issue: 4 year: 2009 ident: 302_CR8 publication-title: ACM Trans Knowl Discov Data – ident: 302_CR16 doi: 10.1137/1.9781611972740.14 – ident: 302_CR49 – ident: 302_CR40 doi: 10.1109/ICDMW.2008.93 – volume-title: Handbook of matrices year: 1997 ident: 302_CR27 – volume-title: Kalman filtering and neural networks year: 2001 ident: 302_CR19 doi: 10.1002/0471221546 – ident: 302_CR21 – ident: 302_CR9 – ident: 302_CR14 doi: 10.1109/ASONAM.2010.17 – ident: 302_CR44 doi: 10.1145/1281192.1281266 – ident: 302_CR46 doi: 10.1145/1401890.1401972 – volume: 19 start-page: 119 issue: 033 year: 2009 ident: 302_CR12 publication-title: Chaos – volume: 10 start-page: 603 issue: 5 year: 2003 ident: 302_CR24 publication-title: J Empir Financ doi: 10.1016/S0927-5398(03)00007-0 – volume: 7 start-page: 339 issue: 2 year: 2009 ident: 302_CR45 publication-title: J Bioinforma Comput Biol doi: 10.1142/S0219720009004114 – ident: 302_CR3 doi: 10.1007/s13278-012-0058-8 – ident: 302_CR34 doi: 10.1073/pnas.0601602103 – volume-title: Forecasting, structural time series models and the Kalman filter year: 1989 ident: 302_CR17 – ident: 302_CR41 doi: 10.1016/0377-0427(87)90125-7 – ident: 302_CR53 – volume-title: An introduction to multivariate statistical analysis year: 2003 ident: 302_CR2 – ident: 302_CR57 – volume: 43 start-page: 113 issue: 1 year: 2010 ident: 302_CR36 publication-title: Pattern Recog doi: 10.1016/j.patcog.2009.06.001 – ident: 302_CR37 – ident: 302_CR33 – volume: 106 start-page: 15274 issue: 36 year: 2009 ident: 302_CR10 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.0900282106 – ident: 302_CR28 – volume: 4 start-page: 32 issue: 1 year: 2005 ident: 302_CR42 publication-title: Stat Appl Genet Mol Biol doi: 10.2202/1544-6115.1175 – ident: 302_CR11 doi: 10.1109/WI.2006.118 – volume: 328 start-page: 876 issue: 5980 year: 2010 ident: 302_CR32 publication-title: Science doi: 10.1126/science.1184819 – volume: 17 start-page: 395 issue: 4 year: 2007 ident: 302_CR48 publication-title: Stat Comput doi: 10.1007/s11222-007-9033-z – ident: 302_CR50 doi: 10.1145/1835804.1835877 – volume-title: The elements of statistical learning: data mining, inference, and prediction year: 2001 ident: 302_CR18 doi: 10.1007/978-0-387-21606-5 – volume: 22 start-page: 888 issue: 8 year: 2000 ident: 302_CR43 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.868688 – ident: 302_CR58 doi: 10.1145/1835804.1835940 – ident: 302_CR25 doi: 10.1145/1014052.1014129 – ident: 302_CR54 – volume: 33 start-page: 1417 issue: 6 year: 2004 ident: 302_CR6 publication-title: SIAM J Comput doi: 10.1137/S0097539702418498 – volume: 82 start-page: 157 issue: 2 year: 2011 ident: 302_CR56 publication-title: Mach Learn doi: 10.1007/s10994-010-5214-7 – ident: 302_CR51 doi: 10.1137/1.9781611972771.12 – ident: 302_CR13 – ident: 302_CR15 doi: 10.1093/bioinformatics/btl242 |
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| SubjectTerms | Algorithms Artificial Intelligence Chemistry and Earth Sciences Clustering Computer Science Data Mining and Knowledge Discovery Data smoothing Datasets Estimates Experiments Information Storage and Retrieval Noise Physics Signal processing Statistics for Engineering |
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