基于特征的时间序列聚类方法研究进展

doi:10.11820/dlkxjz.2012.10.008

鍦扮悊绉戝杩涘睍鈥衡�� 2012, Vol. 31 鈥衡�� Issue (10): 1307-1317.DOI: 10.11820/dlkxjz.2012.10.008

鈥� 妯″瀷涓庢柟娉� 鈥� 涓婁竴绡� 涓嬩竴绡�

鍩轰簬鐗瑰緛鐨勬椂闂村簭鍒楄仛绫绘柟娉曠爺绌惰繘灞�

瀹嬭緸, 瑁撮煬

涓浗绉戝闄㈠湴鐞嗙瀛︿笌璧勬簮鐮旂┒鎵�璧勬簮涓庣幆澧冧俊鎭郴缁熷浗瀹堕噸鐐瑰疄楠屽, 鍖椾含100101

鏀剁鏃ユ湡:2011-10-01 淇洖鏃ユ湡:2012-03-01 鍑虹増鏃ユ湡:2012-10-25 鍙戝竷鏃ユ湡:2012-10-25
閫氳浣滆��: 瑁撮煬(1972-),鐢�,鍓爺绌跺憳,涓昏浠庝簨绌洪棿鏁版嵁鎸栨帢鍜岀┖闂翠俊鎭粺璁＄瓑鏂归潰鐨勭爺绌躲�侲-mail:peit@lreis.ac.cn
浣滆�呯畝浠�:瀹嬭緸(1986-),鐢�,鍗氬＋鐮旂┒鐢�,涓昏鐮旂┒鏂瑰悜涓虹┖闂存暟鎹寲鎺樸�侲-mail:songc@lreis.ac.cn
鍩洪噾璧勫姪:
涓浗绉戝闄㈢煡璇嗗垱鏂板伐绋嬮噸瑕佹柟鍚戦」鐩�(KZCX2-YW-QN303);涓浗绉戝闄㈠湴鐞嗚祫婧愭墍鑷富閮ㄧ讲鍒涙柊椤圭洰(200905004);863 椤圭洰(2009AA12Z227)銆�

Research Progress in Time Series Clustering Methods Based on Characteristics

SONG Ci, PEI Tao

State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2011-10-01 Revised:2012-03-01 Online:2012-10-25 Published:2012-10-25

鎽樿/Abstract

鎽樿锛� 鏃堕棿搴忓垪鑱氱被鍙互鏍规嵁鐩镐技鎬у皢瀵硅薄闆嗗垎涓轰笉鍚岀殑缁�, 浠庤�屽弽鏄犲嚭鍚岀粍瀵硅薄鐨勭浉浼兼�х壒寰佸拰涓嶅悓缁勫璞′箣闂寸殑宸紓鐗瑰緛銆傚綋搴忓垪缁村害杈冮珮鏃�, 浼犵粺鐨勬椂闂村簭鍒楄仛绫绘柟娉曞鏄撳彈鍣０褰卞搷, 闅句互瀹氫箟鍚堥�傜殑鐩镐技鎬у害閲�, 鑱氱被缁撴灉寰�寰�鎰忎箟涓嶆槑纭�傚綋鏁版嵁鏈夌己澶辨垨涓嶇瓑闀挎椂, 鑱氱被鏂规硶涔熼毦浠ュ疄鏂姐�傚熀浜庝笂杩伴棶棰�, 涓�浜涘鑰呮彁鍑轰簡鍩轰簬鐗瑰緛鐨勬椂闂村簭鍒楄仛绫绘柟娉�, 涓嶄粎鍙互瑙ｅ喅涓婅堪闂, 杩樺彲浠ュ彂鐜板簭鍒楁湰璐ㄧ壒寰佺殑鐩镐技鎬с�傛湰鏂囨牴鎹椂闂村簭鍒楃殑涓嶅悓鐗瑰緛, 缁艰堪浜嗗熀浜庣壒寰佺殑鏃堕棿搴忓垪鑱氱被鏂规硶鐨勭爺绌惰繘灞�, 骞惰繘琛屼簡鍒嗘瀽鍜岃瘎杩�;鏈�鍚庡鏈潵鐮旂┒杩涜浜嗗睍鏈涖��

鍏抽敭璇�: 鑱氱被, 鏃堕棿搴忓垪, 鏃堕棿搴忓垪鐗瑰緛, 鏁版嵁鎸栨帢

Abstract: As terabyte time series data pour into the world, more and more attentions have been paid to the technique of analyzing this data. To understand discrepancy between these data, time series clustering methods have been used to divide them into different groups by similarities. Due to high dimension of time series, the traditional clustering methods for static data is not valid for time series clustering problem when they are susceptible to noise, and can hardly define suitable similarity which are prone to a meaningless result. It is also vexatious for many other methods to solve the clustering problem with missing or unequal data. Time series clustering methods based on characteristics could deal with these problems and discover the essential similarities of time series in all directions. According to characteristics of time series, this paper aimed to review the research progress of characteristics-based clustering methods for time series. Firstly, we introduced the definition and classified the different characteristics of time series. Then we reviewed different time series clustering methods based on characteristics and summarized the generality of each method. Finally we discussed some deficiencies of existing methods, and predicted the future of the relative research.

Key words: characteristics of time series, clustering, data mining, time series

瀹嬭緸, 瑁撮煬. 鍩轰簬鐗瑰緛鐨勬椂闂村簭鍒楄仛绫绘柟娉曠爺绌惰繘灞昜J]. 鍦扮悊绉戝杩涘睍, 2012, 31(10): 1307-1317.

SONG Ci, PEI Tao. Research Progress in Time Series Clustering Methods Based on Characteristics[J]. PROGRESS IN GEOGRAPHY, 2012, 31(10): 1307-1317.

0
/ / 鎺ㄨ崘

瀵煎嚭寮曠敤绠＄悊鍣� EndNote|Ris|BibTeX

閾炬帴鏈枃: https://www.progressingeography.com/CN/10.11820/dlkxjz.2012.10.008

https://www.progressingeography.com/CN/Y2012/V31/I10/1307

鍙傝�冩枃鐚�

[1] Shumway R H, Stoffer D S. Time Series Analysis and ItsApplications with R Examples. New York: Springer,2009.

[2] Han J W, Kamber M. Data Mining: Concepts and techniques.Singapore: Elsevier, 2006.

[3] Košmelj K, Batagelj V. Cross-sectional approach forclustering time varying data. Journal of Classification1990, 7: 99-109.

[4] Balasubramaniyan R, Hüllermeier E, Weskamp N, et al.Clustering of gene expression data using a localshape-based similarity measure. Bioinformatics 2005, 21(7): 1069-1077.

[5] Liao T W. Clustering of time series data: A survey. PatternRecognition 2005, 38(11): 1857-1874.

[6] Díaz S P, Vilar J A. Comparing several parametric andnonparametric approaches to time series clustering: Asimulation study. Journal of Classification, 2010, 27(3):333-362.

[7] Keogh E J, Pazzani M J. An enhanced representation oftime series which allows fast and accurate classification,Clustering and Relevance Feedback//Procs. of the 4thConference on Knowledge Discovery in Databases,1998: 239-241.

[8] Chen Y G, Nascimento M A, Ooi B C, et al. SpADe: Onshape-based pattern detection in streaming time series//Proceedings of the 23rd International Conference on DataEngineering, IEEE, 2007: 786-795.

[9] Wang X Z, Smith K, Hyndman R. Characteristic-basedclustering for time series data. Data Mining and KnowledgeDiscovery, 2006, 13(3): 335-364.

[10] Rose O. Estimation of the Hurst Parameter ofLong-Range Dependent Time Series. Research Report,1996.

[11] Hilborn R C, Ottino J M, Shinbrot T. Chaos and nonlineardynamics: An introduction for scientists and engineers.AIChE Journal 1995, 41(7): 1831-1832.

[12] Tian Z, Raghu R, Miron L. BIRCH: An efficient dataclustering method for very large databases. SIGMODRec, 1996, 25(2): 103-114.

[13] Karypis G, Han S, Kumar V. Chameleon: Hierarchicalclustering using dynamic modeling. IEEE Computer,1999, 32(8): 68-75.

[14] Ankerst M, Breunig M M, Kriegel H P, et al. OPTICS:Ordering points to identify the clustering structure. SIGMODRec, 1999, 28(2): 49-60.

[15] Wang W, Yang J, Muntz R. STING: A statistical informationgrid approach to spatial data mining//Proceedings ofthe 23rd Conference on VLDB, 1997: 186-195.

[16] Biernacki C, Celeux G, Govaert G. Assessing a mixturemodel for clustering with the integrated completed likelihood.IEEE Trans, 2000, 22(7): 719-725.

[17] Keogh E, Ratanamahatana C A. Exact indexing of dynamictime warping. Knowledge and Information Systems,2005, 7(3): 358-386.

[18] Möller-Levet C S, Klawonn F, Cho K H, et al. Clusteringof unevenly sampled gene expression time-series data.Fuzzy Sets and Systems, 2005, 152(1): 49-66.

[19] Möller-Levet C S, Klawonn F, Cho K H, et al. Fuzzyclustering of short time-series and unevenly distributedsampling points//Proceedings of the 5th InternationalSymposium on Intelligent Data Analysis, Berlin, Germany,August 28-30, 2003.

[20] Fu T C, Chung F L, Vincent N, et al. Pattern discoveryfrom stock time series using self-organizing maps//KDD2001 Workshop on Temporal Data Mining. San Francisco,2001: 27-37.

[21] Hsu K C, Li S T. Clustering spatial-temporal precipitationdata using wavelet transform and self-organizingmap neural network. Advances in Water Resources 2010,33(2): 190-200.

[22] Lee J G, Han J W, Whang K Y. Trajectory clustering: apartition-and-group framework. Proceedings of ACMSIGMOD International Conference on Management ofData, 2007: 593-604.

[23] Nanopoulos A, Alcock R, Manolopoulos Y. Featurebasedclassification of time-series data. International Journalof Computer Research, 2001: 49-61.

[24] Ouyang R, Ren L, Cheng W, et al. Similarity search andpattern discovery in hydrological time series data mining.Hydrological Processes, 2010, 24(9): 1198-1210.

[25] Kontaki M, Papadopoulos A N, Manolopoulos Y, et al.Continuous trend-based clustering in data streams. DataWarehousing and Knowledge Discovery, 2008, 5182:251-262.

[26] Kumar M, Patel N R, Woo J. Clustering seasonality patternsin the presence of errors. in ACM KDD ConferenceProceedings, 2002: 557-563.

[27] Wang X, Wirth A, Wang L. Structure-based statisticalfeatures and multivariate time series clustering//Proceedingsof the Seventh IEEE International Conference on DataMining, 2007: 351-360.

[28] Caiado J, Crato N, Peña D. A periodogram-based metricfor time series classification. Computational Statistics &Data Analysis 2006, 50(10): 2668-2684.

[29] Kakizawa Y, Shumway R H, Taniguchi M. Discriminationand Clustering for Multivariate Time Series. J. Amer.Stat. Assoc, 1998, 93(441): 328-340.

[30] Shumway R H. Time-frequency clustering and discriminantanalysis. Statistics & Probability Letters, 2003, 63(3): 307-314.

[31] Alonso A M, Berrendero J R, Hernández A, et al. Timeseries clustering based on forecast densities. ComputationalStatistics & Data Analysis, 2006, 51(2): 762-776.

[32] Singhal A, Seborg D E. Clustering multivariate time-seriesdata. Journal of Chemometrics, 2005, 19(8):427-438.

[33] Keogh E, Kasetty S. On the need for time series datamining benchmarks: A survey and empirical demonstration.Data Mining and Knowledge Discovery 2003, 7(4):349-371.

[34] Piccolo D. A distance measure for classifying ARIMAmodels. Journal of Time Series Analysis, 1990, 11(2):153-164.

[35] Maharaj E A. Cluster of time series. Journal of Classification,2000, 17(2): 297-314.

[36] Maharaj E A. Comparison and classification of stationarymultivariate time series. Pattern Recognition, 1999,32(7): 1129-1138.

[37] Ramoni M, Sebastiani P, Cohen P. Bayesian Clusteringby Dynamics. Machine Learning, 2002, 47(1): 91-121.

[38] Ramoni M, Sebastiani P, Cohen P. Multivariate clusteringby dynamics//Proceedings of the Seventeenth NationalConference on Artificial Intelligence, 2000: 633-638.

[39] Xiong Y, Yeung D Y. Mixtures of ARMA Models forModel-Based Time Series Clustering. Proceedings ofIEEE International Conference on Data Mining, 2002:717-720.

[40] Bicego M, Murino V, Figueiredo M A T. Similarity-based clustering of sequences using hidden Markovmodels. Machine Learning and Data Mining in PatternRecognition, 2003, 2734: 86-95.

[41] Rabiner L R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedingsof the IEEE 1989, 77(2): 257-286

[42] Oates T, Firoiu L, Cohen P R. Clustering time serieswith hidden markov models and dynamic time warping.Proceedings of the IJCAI-99 Workshop on Neural, Symbolic,and Reinforcement Learning Methods for SequenceLearning, 1999.

[43] Li C, Biswas G. Temporal Pattern Generation Using HiddenMarkov Model Based Unsupervised Classification.Advances in Intelligent Data Analysis., 1999: 245-256.

[44] Li C, Biswas G, Dale M, et al. Building models of ecologicaldynamics using HMM based temporal data clustering:A preliminary study. Advances in Intelligent DataAnalysis, 2001: 53-62, doi: 10.1007/3-540-44816-0_6.

[45] Jain A K. Data clustering: 50 years beyond K-means.Pattern Recognition Letters, 2009, 31(8): 651-666.

[46] Wang N Y, Chen S M. Temperature prediction and TAIFEXforecasting based on automatic clustering techniquesand two-factors high-order fuzzy time series. ExpertSystems with Applications, 2009, 36(2): 2143-2154.

[47] FrÄuhwirth-Schnatter S. Model-based clustering of timeseries: A rview from a Bayesian perspective. Manuscript,2011.

[48] Pakhira M K, Bandyopadhyay S, Maulik U. Validity indexfor crisp and fuzzy clusters. Pattern Recognition2004, 37(3): 487-501.

鍩轰簬鐗瑰緛鐨勬椂闂村簭鍒楄仛绫绘柟娉曠爺绌惰繘灞�

Research Progress in Time Series Clustering Methods Based on Characteristics

PDF (PC)

鍙鍖�

琚紩娆℃暟

鎽樿/Abstract

寮曠敤鏈枃

浣跨敤鏈枃

鍙傝�冩枃鐚�

鐩稿叧鏂囩珷 15

缂栬緫鎺ㄨ崘

Metrics

鏈枃璇勪环

[1]	鍒樻捣鐚�, 閮戠憺濠�, 鍕鹃箯, 绋嬮挵, 鐔婃磥闃�. 鍩轰簬娲诲姏涓夎妯″瀷鐨勪腑鍥藉煄甯傛椿鍔涜瘎浼�[J]. 鍦扮悊绉戝杩涘睍, 2024, 43(6): 1118-1132.
[2]	鏇圭伩, 寮犳案鍕�, 鍒樼帀, 寮犱笘褰�, 鍒樻檽娲�, 鐜嬪浗搴�. 榛勬渤姘存簮娑靛吇鍖烘簮澶村皬娴佸煙寰勬祦鎯呭娍鍙樺寲鐨勬ā寮忚鲸璇�[J]. 鍦扮悊绉戝杩涘睍, 2023, 42(9): 1667-1676.
[3]	榫欏啲骞�, 寰愰摥鎭�, 鏌虫灄. 涓浗閿�鍞被鍋囪嵂鐘姜鐨勬椂绌烘紨鍖栫壒寰佸強鍦板煙绫诲瀷鐮旂┒[J]. 鍦扮悊绉戝杩涘睍, 2023, 42(5): 944-959.
[4]	绋嬮洩鍏�, 鏂瑰彾鏋�, 鑻忛洩鏅�, 鏉庣粡榫�. 涓浗涓滈儴娌挎捣5澶у煄甯傜兢鏃呮父娴佺綉缁滅粨鏋勭┖闂村垎甯冪壒寰佺爺绌�[J]. 鍦扮悊绉戝杩涘睍, 2021, 40(6): 948-957.
[5]	浠濆痉, 鍛ㄥ績鐏�, 榫氬拸鍠�. 鍩轰簬澶ф暟鎹殑涓婃捣甯傚叡浜苯杞﹀嚭琛屾ā寮忕爺绌�[J]. 鍦扮悊绉戝杩涘睍, 2021, 40(12): 2035-2047.
[6]	榛勮幑娉�, 閭辩偝鏂�, 浣曠帀鑺�, 寮犵弬, 閭瑰嚖涓�. 涓滃寳鍦板尯姘寸ɑ鎵╁紶鐨勬捣鎷斾紭鍔垮尯闂村垎鏋�[J]. 鍦扮悊绉戝杩涘睍, 2020, 39(9): 1557-1564.
[7]	鎴胯壋鍒�,鍒樻湰鍩�,鍒樺缓蹇�. 鍐滀笟澶氬姛鑳界殑鍦板煙绫诲瀷涓庝紭鍖栫瓥鐣モ�斺�斾互鍚夋灄鐪佷负渚�[J]. 鍦扮悊绉戝杩涘睍, 2019, 38(9): 1349-1360.
[8]	椹槑娓�, 琚佹, 钁涘叏鑳�, 琚佹枃, 鏉ㄦ灄鐢�, 鏉庢眽闈�, 鏉庤悓. 鈥滀竴甯︿竴璺�濊嫢骞插尯鍩熺ぞ浼氬彂灞曟�佸娍澶ф暟鎹垎鏋�[J]. 鍦扮悊绉戝杩涘睍, 2019, 38(7): 1009-1020.
[9]	閽熺倻鑿�, 鐜嬪痉. 鍩轰簬灞呮皯琛屼负鍛ㄦ湡鐗瑰緛鐨勫煄甯傜┖闂寸爺绌�[J]. 鍦扮悊绉戝杩涘睍, 2018, 37(8): 1106-1118.
[10]	鏉庝箙鏋�, 浣欏崕椋�, 浠樿繋鏄�, 璧佃��榫�. 骞夸笢鐪佲�滀汉鍙ｂ�旂粡娴庘�斿湡鍦扳�旂ぞ浼氣�旂敓鎬佲�濆煄甯傚寲鍗忚皟搴︽椂绌哄彉鍖栧強鍏惰仛绫绘ā寮�[J]. 鍦扮悊绉戝杩涘睍, 2018, 37(2): 287-298.
[11]	鍏抽洩宄�, 鏇惧畤濯�. 鏃剁┖澶ф暟鎹儗鏅笅骞惰鏁版嵁澶勭悊鍒嗘瀽鎸栨帢鐨勮繘灞曞強瓒嬪娍[J]. 鍦扮悊绉戝杩涘睍, 2018, 37(10): 1314-1327.
[12]	绉︽槅, 鍛ㄥ媿, 寰愭簮娉�, 寰愰洴濠�, 缃楄悕. 鍩庡競浜ら�氱儹鐐瑰尯鍩熺殑绌洪棿浜や簰缃戠粶鍒嗘瀽[J]. 鍦扮悊绉戝杩涘睍, 2017, 36(9): 1149-1157.
[13]	閮�濇収, 鏂囪仾鑱�, 浣曚簯, 瑁撮煬. 灞呮皯鍑鸿娲诲姩鐗瑰緛涓庢敹鍏ユ按骞崇殑鍏崇郴鈥斺�斾互涓婃捣甯備负渚�[J]. 鍦扮悊绉戝杩涘睍, 2017, 36(9): 1158-1166.
[14]	鏉ㄥ井鐭�, 閮棪鎬�, 閫嚂鐜�, 鐜嬪痉寮�, 鏈辨槧绉�, 寮犲疂绉�. 鍩轰簬澶ф暟鎹殑鏂囧寲閬椾骇璁ょ煡鍒嗘瀽鏂规硶鈥斺�斾互鍖椾含鏃у煄涓酱绾夸负渚�[J]. 鍦扮悊绉戝杩涘睍, 2017, 36(9): 1111-1118.
[15]	寮犵彛, 闄堝仴鐠�, 榛勯噾宸�, 浜庨噸閲�, 闄堢鏂�. 鍩轰簬绌洪棿鑱氱被鏂规硶鐨勪含娲ュ唨鍩庡競缇ゅ灞傜骇绌洪棿缁撴瀯鐮旂┒[J]. 鍦扮悊绉戝杩涘睍, 2017, 36(11): 1359-1367.