{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T23:53:01Z","timestamp":1770508381439,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2008,3,11]],"date-time":"2008-03-11T00:00:00Z","timestamp":1205193600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2009,1]]},"DOI":"10.1007\/s10115-008-0125-7","type":"journal-article","created":{"date-parts":[[2008,3,10]],"date-time":"2008-03-10T10:50:50Z","timestamp":1205146250000},"page":"1-27","source":"Crossref","is-referenced-by-count":21,"title":["Pattern-based time-series subsequence clustering using radial distribution functions"],"prefix":"10.1007","volume":"18","author":[{"given":"Anne M.","family":"Denton","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher A.","family":"Besemann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dietmar H.","family":"Dorr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2008,3,11]]},"reference":[{"key":"125_CR1","unstructured":"Berndt D, Clifford J (1996) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, chapter Finding patterns in time series: a dynamic programming approach, pp 229\u2013248"},{"key":"125_CR2","doi-asserted-by":"crossref","unstructured":"Breunig M, Kriegel H-P, Ng R, Sander J (2000) LOF: identifying density-based local outliers. In: Proceedings of 5th ACM SIGMOD international conference on mangement of data, pp 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"125_CR3","doi-asserted-by":"crossref","unstructured":"Chen J (2005) Making subsequence time series clustering meaningful. In: Proceedings of the 5th IEEE international conference on data mining (ICDM\u201905), Houston, pp 114\u2013121","DOI":"10.1109\/ICDM.2005.91"},{"issue":"3","key":"125_CR4","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10115-006-0042-6","volume":"11","author":"J Chen","year":"2007","unstructured":"Chen J (2007a) Making clustering in delay-vector space meaningful. Knowl Inf Syst 11(3): 369\u2013385","journal-title":"Knowl Inf Syst"},{"key":"125_CR5","unstructured":"Chen J (2007b) Useful clustering outcomes from meaningful time series clustering. In: Proceedings of the Australasian data mining conference, Gold Coast, Australia"},{"issue":"5","key":"125_CR6","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5): 603\u2013619","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"125_CR7","unstructured":"Das G, Lin K-I, Mannila H et al (1998) Rule discovery from time series. In: Proceedings of the 4th ACM SIGKDD international conference on data mining, Rio de Janeiro, Brazil"},{"issue":"2","key":"125_CR8","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1063\/1.1531823","volume":"74","author":"C Daw","year":"2003","unstructured":"Daw C, Finney C, Tracy E (2003) A review of symbolic analysis of experimental data. Rev Sci Instrum 74(2): 915\u2013930","journal-title":"Rev Sci Instrum"},{"key":"125_CR9","unstructured":"Denton A (2004) Density-based clustering of time series subsequences. In: Proceedings of the 3rd workshop on mining temporal and sequential data (TDM 04) in conj. with the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle"},{"key":"125_CR10","doi-asserted-by":"crossref","unstructured":"Denton A (2005) Kernel-density-based clustering of time series subsequences using a continuous random-walk noise model. In: Proceedings of the 5th IEEE international conference on data mining (ICDM\u201905), Houston, pp 122\u2013129","DOI":"10.1109\/ICDM.2005.84"},{"key":"125_CR11","doi-asserted-by":"crossref","unstructured":"Ernst J, Nau G, Bar-Joesph Z (2005) Clustering short time series gene expression data, Bioinformatics 21(Supplement 1)","DOI":"10.1093\/bioinformatics\/bti1022"},{"key":"125_CR12","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining"},{"key":"125_CR13","doi-asserted-by":"crossref","unstructured":"Gavrilov M, Anguelov D, Indyk P, Motwani R (2000) Mining the stock market (extended abstract): which measure is best?. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, pp 487\u2013496","DOI":"10.1145\/347090.347189"},{"key":"125_CR14","doi-asserted-by":"crossref","unstructured":"Goldberger A, Amaral L, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation 101(23):e215\u2013e220. Circulation Electronic Pages: [ http:\/\/circ.ahajournals.org\/cgi\/content\/full\/101\/23\/e215 ]","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"125_CR15","doi-asserted-by":"crossref","unstructured":"Goldin D, Mardales R, Nagy G (2006) In search of meaning for time series subsequence clustering: Matching algorithms based on a new distance measure. In: Proceedings of the Conference on Information and Knowledge Management, Washington, DC","DOI":"10.1145\/1183614.1183666"},{"key":"125_CR16","unstructured":"Han J, Kamber M (2006) Data Mining: Concepts and Techniques 2nd edn. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers"},{"issue":"4","key":"125_CR17","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s10115-003-0086-9","volume":"5","author":"A Hinneburg","year":"2003","unstructured":"Hinneburg A, Keim D (2003) A general approach to clustering in large databases with noise. Knowl Inf Syst 5(4): 387\u2013415","journal-title":"Knowl Inf Syst"},{"key":"125_CR18","unstructured":"Ide T (2006) Why does subsequence time-series clustering produce sine waves?. In: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp 311\u2013322"},{"key":"125_CR19","unstructured":"Ihler A (accessed 04\/2003), Kernel density estimation toolbox for matlab (r13). [ http:\/\/ttic.uchicago.edu\/~ihler\/code\/kde.php ]"},{"key":"125_CR20","volume-title":"Nonlinear time series analysis","author":"H Kantz","year":"1999","unstructured":"Kantz H, Schreiber T (1999) Nonlinear time series analysis. Cambridge University Press, Cambridge"},{"key":"125_CR21","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316801","volume-title":"Finding groups in data: an introduction to cluster analysis","author":"L Kaufman","year":"1990","unstructured":"Kaufman L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York"},{"key":"125_CR22","unstructured":"Keogh E, Folias T (accessed 2003) The ucr time series data mining archive. [ http:\/\/www.cs.ucr.edu\/~eamonn\/TSDMA\/index.html ]"},{"key":"125_CR23","doi-asserted-by":"crossref","unstructured":"Keogh E, Lin J, Truppel W (2003) Clustering of time series subsequences is meaningless: implications for previous and future research. In: Proceedings of the IEEE international conference on data mining, Melbourne, pp 115\u2013122","DOI":"10.1109\/ICDM.2003.1250910"},{"key":"125_CR24","doi-asserted-by":"crossref","unstructured":"Keogh E, Pazzani M (2000) Scaling up dynamic time warping for datamining applications. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA, pp 285\u2013289","DOI":"10.1145\/347090.347153"},{"issue":"2","key":"125_CR25","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: a novel symbolic representation of time series. Data Mining Knowl Discovery 15(2): 107\u2013144","journal-title":"Data Mining Knowl Discovery"},{"issue":"5","key":"125_CR26","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TKDE.2002.1033770","volume":"14","author":"R Ng","year":"2002","unstructured":"Ng R, Han J (2002) Clarans: A method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5): 1003\u20131016","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"125_CR27","doi-asserted-by":"crossref","unstructured":"Papadimitriou S, Kitagawa H, Gibbons P, Faloutsos C (2003) Loci: Fast outlier detection using the local correlation integral. In: Proceedings of the 19th international conference on data engineering (ICDE), pp 315\u2013326","DOI":"10.1109\/ICDE.2003.1260802"},{"key":"125_CR28","doi-asserted-by":"crossref","unstructured":"Patel P, Keogh E, Lin J, Lonardi S (2002) Mining motifs in massive time series databases. In: Proceedings of the IEEE international conference on data mining, Maebashi City, Japan","DOI":"10.1109\/ICDM.2002.1183925"},{"key":"125_CR29","doi-asserted-by":"crossref","unstructured":"Peker K (2005) Subsequence time series (sts) clustering techniques for meaningful pattern discovery. In: Proceedings of the IEEE KIMAS Conference","DOI":"10.1109\/KIMAS.2005.1427109"},{"key":"125_CR30","unstructured":"Saito N (1995) Local feature extraction and its application using a library of bases. PhD Thesis"},{"key":"125_CR31","unstructured":"Simon G, Lee J, Verleysen M (2005) On the need of unfolding preprocessing for time series clustering. In: Proceedings of Workshop on Self-Organizing Maps (WSOM\u201905), Paris, France, pp 251\u2013258"},{"key":"125_CR32","doi-asserted-by":"crossref","unstructured":"Verleysen M, Fran\u00e7ois D (2005) The curse of dimensionality in data mining and time series prediction, In: Cabestany J, Prieto A, Sandoval F (eds) Computational Intelligence and Bioinspired Systems, Lecture Notes in Computer Science 3512. Springer, Heidelberg pp 758\u2013770","DOI":"10.1007\/11494669_93"},{"key":"125_CR33","doi-asserted-by":"crossref","unstructured":"Vlachos M, Gunopoulos D, Kollios G (2002) Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering (ICDE\u201902), San Jose","DOI":"10.1109\/ICDE.2002.994784"},{"key":"125_CR34","unstructured":"Weisstein E (2003) Hypersphere, in MathWorld\u2014A Wolfram Web Resource, [ http:\/\/mathworld.wolfram.com\/Hypersphere.html ]"},{"key":"125_CR35","doi-asserted-by":"crossref","unstructured":"Yankov D, Keogh E, Medina J et al (2007) Detecting time series motiv under uniform scaling, In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose","DOI":"10.1145\/1281192.1281282"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-008-0125-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-008-0125-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-008-0125-7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,29]],"date-time":"2019-05-29T10:10:17Z","timestamp":1559124617000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-008-0125-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,3,11]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2009,1]]}},"alternative-id":["125"],"URL":"https:\/\/doi.org\/10.1007\/s10115-008-0125-7","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,3,11]]}}}