Abstract
A recurring problem in the second part of this book is the problem of discovering recurrent patterns in long multidimensional time-series. This chapter introduces some of the algorithms that can be employed in solving this kind of problems for both discrete and continuous time-series.
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References
Buhler J, Tompa M (2002) Finding motifs using random projections. J Comput Biol 9(2):225–242
Catalano J, Armstrong T, Oates T (2006) Discovering patterns in real-valued time series. In: PKDD’06: knowledge discovery in databases, pp 462–469
Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: KDD’03: the 9th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, USA, pp 493–498. http://doi.acm.org/10.1145/956750.956808
Jensen KL, Styczynxki MP, Rigoutsos I, Stephanopoulos GN (2006) A generic motif discovery algorithm for sequenctial data. BioInformatics 22(1):21–28
Kulic D, Nakamura Y (2008) Incremental learning and memory consolidation of whole body motion patterns. In: International conference on epigenetic robotics, pp 61–68
Li Y, Lin J (2010) Approximate variable-length time series motif discovery using grammar inference. In: The 10th international workshop on multimedia data mining, ACM, pp 101–109
Lin J, Keogh E, Lonardi S, Patel P (2002) Finding motifs in time series. In: The 2nd workshop on temporal data mining, at the 8th ACM SIGKDD, pp 53–68
Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. In: KDD’07: international conference on data mining and knowledge discovery, vol 15(2), pp 107–144. doi:10.1007/s10618-007-0064-z
Minnen D, Starner T, Essa IA, Isbell Jr CL (2007) Improving activity discovery with automatic neighborhood estimation. In: IJCAI’07: 16th international joint conference on artificial intelligence, vol 7, pp 2814–2819
Mohammad Y, Nishida T (2011) On comparing SSA-based change point discovery algorithms. In: SII’11: IEEE/SICE international symposium on system integration, pp 938–945
Mohammad Y, Nishida T (2012a) Fluid imitation: discovering what to imitate. Int J Social Robot 4(4):369–382
Mohammad Y, Nishida T (2012b) Unsupervised discovery of basic human actions from activity recording datasets. In: SII’12: IEEE/SICE international symposium on system integration, IEEE, pp 402–409
Mohammad Y, Nishida T (2009) Constrained motif discovery in time series. New Gener Comput 27(4):319–346
Mohammad Y, Ohmoto Y, Nishida T (2012) GSteX: greedy stem extension for free-length constrained motif discovery. In: IEA/AIE’12: the international conference on industrial, engineering, and other applications of applied intelligence, pp 417–426
Mohammad Y, Nishida T (2015a) Exact multi-length scale and mean invariant motif discovery. Appl Intell 1–18. doi:10.1007/s10489-015-0684-8
Mohammad Y, Nishida T (2015b) Shift density estimation based approximately recurring motif discovery. Appl Intell 42(1):112–134
Mueen A, Keogh E, Bigdely-Shamlo N (2009a) Finding time series motifs in disk-resident data. In: ICDM’09: IEEE international conference on data mining, IEEE, pp 367–376
Mueen A, Keogh E, Zhu Q, Cash S, Westover B (2009b) Exact discovery of time series motifs. In: SDM’09: SIAM international conference on data mining, pp 473–484
Nevill-Manning CG, Witten IH (1997) Identifying hierarchical strcture in sequences: a linear-time algorithm. J Artif Intell Res 7:67–82
Nishida T, Nakazawa A, Ohmoto Y, Mohammad Y (2014) Conversation quantization. Springer, chap 4, pp 103–130
Pantic M, Pentland A, Nijholt A, Huang T (2007) Machine understanding of human behavior. In: AI4HC’07: IJCAI 2007 workshop on artificail intelligence for human computing. University of Twente, Centre for Telematics and Information Technology (CTIT), pp 13–24
Pavesi G, Mauri G, Pesole G (2001) Methods for pattern discovery in unaligned biological sequences. Brief Bioinform 2(4):417–431
Pevzner PA, Sze SH et al (2000) Combinatorial approaches to finding subtle signals in dna sequences. In: ISMB’00: the 8th international conference on intelligent systems for molecular biology, vol 8, pp 269–278
Sagot MF (1998) Spelling approximate repeated or common motifs using a suffix tree. In: Theoretical informatics. Springer, pp 374–390
Staden R (1989) Methods for discovering novel motifs in nucleic acid sequences. Comput Appl Biosci 5(4):293–298
Tanaka Y, Iwamoto K, Uehara K (2005) Discovery of time-series motif from multi-dimensional data based on mdl principle. Mach Learn 58(2/3):269–300
Tang H, Liao SS (2008) Discovering original motifs with different lengths from time series. Knowl-Based Syst 21(7):666–671. http://dx.doi.org/10.1016/j.knosys.2008.03.022
Tompa M (1999) An exact method for finding short motifs in sequences, with application to the ribosome binding site problem. In: ISMB’99: the 7th international conference on intelligent systems for molecular biology, vol 99, pp 262–271
Vahdatpour A, Amini N, Sarrafzadeh M (2009) Toward unsupervised activity discovery using multi-dimensional motif detection in time series. In: IJCAI’09: the international joint conference on artificial intelligence, pp 1261–1266
Waterman M, Arratia R, Galas D (1984) Pattern recognition in several sequences: consensus and alignment. Bull Math Biol 46(4):515–527
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Mohammad, Y., Nishida, T. (2015). Motif Discovery. In: Data Mining for Social Robotics. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-25232-2_4
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DOI: https://doi.org/10.1007/978-3-319-25232-2_4
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