-
1
-
-
85196073376
-
-
Mining approximate motifs in time series, in Discovery Science, Secaucus, New Jersey, Springer
-
P. Ferreira, P. Azevedo, C. Silva, and R. Brito, Mining approximate motifs in time series, in Discovery Science, Secaucus, New Jersey, Springer, 2006, 89-101.
-
(2006)
, pp. 89-101
-
-
Ferreira, P.1
Azevedo, P.2
Silva, C.3
Brito, R.4
-
2
-
-
85196118412
-
-
Finding motifs in time series, In Proc. of the 2nd Workshop on Temporal Data Mining, Citeseer
-
J. Lin, E. Keogh, S. Lonardi, and P. Patel, Finding motifs in time series, In Proc. of the 2nd Workshop on Temporal Data Mining, Citeseer, 2002, 53-68.
-
(2002)
, pp. 53-68
-
-
Lin, J.1
Keogh, E.2
Lonardi, S.3
Patel, P.4
-
3
-
-
85196060477
-
-
Probabilistic discovery of time series motifs, In Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM
-
B. Chiu, E. Keogh, and S. Lonardi, Probabilistic discovery of time series motifs, In Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2003, 498.
-
(2003)
, pp. 498
-
-
Chiu, B.1
Keogh, E.2
Lonardi, S.3
-
4
-
-
15544364892
-
Discovery of time-series motif from multi-dimensional data based on mdl principle,
-
2
-
Y. Tanaka, K. Iwamoto, and K. Uehara, Discovery of time-series motif from multi-dimensional data based on mdl principle, Mach Learn 58(2) (2005), 269-300.
-
(2005)
Mach Learn
, vol.58
, pp. 269-300
-
-
Tanaka, Y.1
Iwamoto, K.2
Uehara, K.3
-
5
-
-
85196062409
-
PERUSE: an unsupervised algorithm for finding recurring patterns in time series,
-
T. Oates, PERUSE: an unsupervised algorithm for finding recurring patterns in time series, IEEE ICDM 2 (2002), 5.
-
(2002)
IEEE ICDM
, vol.2
, pp. 5
-
-
Oates, T.1
-
6
-
-
36849013559
-
-
Detecting time series motifs under uniform scaling, In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
-
D. Yankov, E. Keogh, J. Medina, B. Chiu, and V. Zordan, Detecting time series motifs under uniform scaling, In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, 844-853.
-
(2007)
, pp. 844-853
-
-
Yankov, D.1
Keogh, E.2
Medina, J.3
Chiu, B.4
Zordan, V.5
-
7
-
-
84880902891
-
-
Improving activity discovery with automatic neighborhood estimation, Proceedings of the 20th international joint conference on Artifical intelligence, San Francisco, California, Morgan Kaufmann Publishers Inc.
-
D. Minnen, T. Starner, I. Essa, and C. Isbell, Improving activity discovery with automatic neighborhood estimation, Proceedings of the 20th international joint conference on Artifical intelligence, San Francisco, California, Morgan Kaufmann Publishers Inc., 2007, 2814-2819.
-
(2007)
, pp. 2814-2819
-
-
Minnen, D.1
Starner, T.2
Essa, I.3
Isbell, C.4
-
8
-
-
34548085733
-
Efficient mining of understandable patterns from multivariate interval time series,
-
2
-
F. Mörchen and A. Ultsch, Efficient mining of understandable patterns from multivariate interval time series, Data Min Knowl Discov 15(2) (2007), 181-215.
-
(2007)
Data Min Knowl Discov
, vol.15
, pp. 181-215
-
-
Mörchen, F.1
Ultsch, A.2
-
9
-
-
85196075046
-
-
Exact discovery of time series motifs, In Proceedings of the Ninth SIAM International Conference on Data Mining (SDM)
-
A. Mueen, E. Keogh, Q. Zhu, S. Cash, and B. Westover, Exact discovery of time series motifs, In Proceedings of the Ninth SIAM International Conference on Data Mining (SDM), 2009, 473-484.
-
(2009)
, pp. 473-484
-
-
Mueen, A.1
Keogh, E.2
Zhu, Q.3
Cash, S.4
Westover, B.5
-
10
-
-
77956199047
-
-
Online discovery and maintenance of time series motifs, In Proceedings of the sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM
-
A. Mueen and E. Keogh, Online discovery and maintenance of time series motifs, In Proceedings of the sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, 1089-1098.
-
(2010)
, pp. 1089-1098
-
-
Mueen, A.1
Keogh, E.2
-
11
-
-
77951156369
-
-
Finding time series motifs in disk-resident data, In 2009 Ninth IEEE International Conference on Data Mining
-
A. Mueen, E. Keogh, and N. Bigdely-Shamlo, Finding time series motifs in disk-resident data, In 2009 Ninth IEEE International Conference on Data Mining, 2009, 367-376.
-
(2009)
, pp. 367-376
-
-
Mueen, A.1
Keogh, E.2
Bigdely-Shamlo, N.3
-
12
-
-
80155203953
-
-
Multiresolution motif discovery in time series, In Proceedings of the Tenth SIAM International Conference on Data Mining
-
N. Castro and P. Azevedo, Multiresolution motif discovery in time series, In Proceedings of the Tenth SIAM International Conference on Data Mining, 2010, 665-676.
-
(2010)
, pp. 665-676
-
-
Castro, N.1
Azevedo, P.2
-
13
-
-
85196092490
-
-
The UCR Time Series Data Mining Archive, Riverside CA, University of California-Computer Science & Engineering Department
-
E. Keogh and T. Folias, The UCR Time Series Data Mining Archive, Riverside CA, University of California-Computer Science & Engineering Department, 2002.
-
(2002)
-
-
Keogh, E.1
Folias, T.2
-
14
-
-
34848816094
-
Statistical tests to compare motif count exceptionalities,
-
1)
-
S. Robin, S. Schbath, and V. Vandewalle, Statistical tests to compare motif count exceptionalities, BMC Bioinformatics 8(1) 2007, 84.
-
(2007)
BMC Bioinformatics
, vol.8
, pp. 84
-
-
Robin, S.1
Schbath, S.2
Vandewalle, V.3
-
15
-
-
0037174670
-
Network motifs: simple building blocks of complex networks,
-
5594
-
R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, Network motifs: simple building blocks of complex networks, Science 298(5594) (2002), 824.
-
(2002)
Science
, vol.298
, pp. 824
-
-
Milo, R.1
Shen-Orr, S.2
Itzkovitz, S.3
Kashtan, N.4
Chklovskii, D.5
Alon, U.6
-
16
-
-
34249653461
-
Discovering significant patterns,
-
1
-
G. Webb, Discovering significant patterns, Mach Learn 68(1) (2007), 1-33.
-
(2007)
Mach Learn
, vol.68
, pp. 1-33
-
-
Webb, G.1
-
17
-
-
34548784572
-
-
Evaluating protein motif significance measures: a case study on prosite patterns, In IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007)
-
P. G. Ferreira and P. J. Azevedo, Evaluating protein motif significance measures: a case study on prosite patterns, In IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007), 2007, 171-178.
-
(2007)
, pp. 171-178
-
-
Ferreira, P.G.1
Azevedo, P.J.2
-
18
-
-
34047159557
-
Computing exact P-values for DNA motifs,
-
5
-
J. Zhang, B. Jiang, M. Li, J. Tromp, X. Zhang, and M. Zhang, Computing exact P-values for DNA motifs, Bioinformatics 23(5) (2007), 531.
-
(2007)
Bioinformatics
, vol.23
, pp. 531
-
-
Zhang, J.1
Jiang, B.2
Li, M.3
Tromp, J.4
Zhang, X.5
Zhang, M.6
-
19
-
-
66349110658
-
Efficient exact motif discovery,
-
12)
-
T. Marschall and S. Rahmann, Efficient exact motif discovery, Bioinformatics 25(12) 2009, i356.
-
(2009)
Bioinformatics
, vol.25
-
-
Marschall, T.1
Rahmann, S.2
-
20
-
-
34248358126
-
Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics,
-
1)
-
G. Nuel, Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics, Algorithms Mol Biol 1(1) 2006, 5.
-
(2006)
Algorithms Mol Biol
, vol.1
, pp. 5
-
-
Nuel, G.1
-
21
-
-
37849021289
-
Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules,
-
1
-
V. Boeva, J. Clément, M. Régnier, M. Roytberg, and V. Makeev, Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules, Algorithms Mol Biol 2(1) (2007), 13.
-
(2007)
Algorithms Mol Biol
, vol.2
, pp. 13
-
-
Boeva, V.1
Clément, J.2
Régnier, M.3
Roytberg, M.4
Makeev, V.5
-
22
-
-
85196078917
-
-
Mining for unexpected sequential patterns given a Markov model
-
C. Low Kam, A. Mas, and M. Teisseire, Mining for unexpected sequential patterns given a Markov model, 2008. http://www.math.univ-montp2.fr/~mas/lmt_siam09.pdf.
-
(2008)
-
-
Low Kam, C.1
Mas, A.2
Teisseire, M.3
-
23
-
-
33845875680
-
DASS: efficient discovery and p-value calculation of substructures in unordered data,
-
1
-
J. Hollunder, M. Friedel, A. Beyer, C. Workman, and T. Wilhelm, DASS: efficient discovery and p-value calculation of substructures in unordered data, Bioinformatics 23(1) (2007), 77.
-
(2007)
Bioinformatics
, vol.23
, pp. 77
-
-
Hollunder, J.1
Friedel, M.2
Beyer, A.3
Workman, C.4
Wilhelm, T.5
-
24
-
-
0034786443
-
Numerical comparison of several approximations of the word count distribution in random sequences,
-
4
-
S. Robin and S. Schbath, Numerical comparison of several approximations of the word count distribution in random sequences, J Comput Biol 8(4) (2001), 349-359.
-
(2001)
J Comput Biol
, vol.8
, pp. 349-359
-
-
Robin, S.1
Schbath, S.2
-
26
-
-
0034048881
-
An overview on the distribution of word counts in Markov chains,
-
1-2
-
S. Schbath, An overview on the distribution of word counts in Markov chains, J Comput Biol 7(1-2) (2000), 193-201.
-
(2000)
J Comput Biol
, vol.7
, pp. 193-201
-
-
Schbath, S.1
-
27
-
-
84878102536
-
-
Graphrank: statistical modeling and mining of significant subgraphs in the feature space, In Sixth International Conference on Data Mining (ICDM'06)
-
H. He and A. Singh, Graphrank: statistical modeling and mining of significant subgraphs in the feature space, In Sixth International Conference on Data Mining (ICDM'06), 2006, 885-890.
-
(2006)
, pp. 885-890
-
-
He, H.1
Singh, A.2
-
28
-
-
60949112121
-
Mining probabilistic automata: a statistical view of sequential pattern mining,
-
1
-
S. Jacquemont, F. Jacquenet, and M. Sebban, Mining probabilistic automata: a statistical view of sequential pattern mining, Mach Learn 75(1) (2009), 91-127.
-
(2009)
Mach Learn
, vol.75
, pp. 91-127
-
-
Jacquemont, S.1
Jacquenet, F.2
Sebban, M.3
-
29
-
-
57249116908
-
Faster exact Markovian probability functions for motif occurrences: a DFA-only approach,
-
24
-
P. Ribeca and E. Raineri, Faster exact Markovian probability functions for motif occurrences: a DFA-only approach, Bioinformatics 24(24) (2008), 2839.
-
(2008)
Bioinformatics
, vol.24
, pp. 2839
-
-
Ribeca, P.1
Raineri, E.2
-
30
-
-
70849103590
-
Network motifs: mean and variance for the count,
-
1
-
C. Matias, S. Schbath, E. Birmelé, J. Daudin, and S. Robin, Network motifs: mean and variance for the count, REVSTAT Stat J 4(1) (2006), 31-51.
-
(2006)
REVSTAT Stat J
, vol.4
, pp. 31-51
-
-
Matias, C.1
Schbath, S.2
Birmelé, E.3
Daudin, J.4
Robin, S.5
-
31
-
-
39449128256
-
Assessing the exceptionality of network motifs,
-
1
-
F. Picard, J. Daudin, M. Koskas, S. Schbath, and S. Robin, Assessing the exceptionality of network motifs, J Comput Biol 15(1) (2008), 1-20.
-
(2008)
J Comput Biol
, vol.15
, pp. 1-20
-
-
Picard, F.1
Daudin, J.2
Koskas, M.3
Schbath, S.4
Robin, S.5
-
32
-
-
0141463039
-
-
Finding surprising patterns in a time series database in linear time and space, In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
-
E. Keogh, S. Lonardi, and B. Chiu, Finding surprising patterns in a time series database in linear time and space, In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002, 550-556.
-
(2002)
, pp. 550-556
-
-
Keogh, E.1
Lonardi, S.2
Chiu, B.3
-
33
-
-
0042711018
-
On the need for time series data mining benchmarks: a survey and empirical demonstration,
-
4
-
E. Keogh and S. Kasetty, On the need for time series data mining benchmarks: a survey and empirical demonstration, Data Min Knowl Discov 7(4) (2003), 349-371.
-
(2003)
Data Min Knowl Discov
, vol.7
, pp. 349-371
-
-
Keogh, E.1
Kasetty, S.2
-
34
-
-
84867136666
-
Querying and mining of time series data: experimental comparison of representations and distance measures,
-
2
-
H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, Querying and mining of time series data: experimental comparison of representations and distance measures, Proc VLDB Endowment 1(2) (2008), 1542-1552.
-
(2008)
Proc VLDB Endowment
, vol.1
, pp. 1542-1552
-
-
Ding, H.1
Trajcevski, G.2
Scheuermann, P.3
Wang, X.4
Keogh, E.5
-
35
-
-
65449164304
-
-
iSAX: indexing and mining terabyte sized time series, In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
-
J. Shieh and E. Keogh, iSAX: indexing and mining terabyte sized time series, In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, 623-631.
-
(2008)
, pp. 623-631
-
-
Shieh, J.1
Keogh, E.2
-
36
-
-
84880116122
-
Statistics of motifs,
-
S. Schbath, Statistics of motifs, Atelier de Form 1502 (2006).
-
(2006)
Atelier de Form
, vol.1502
-
-
Schbath, S.1
-
37
-
-
85196094466
-
-
DNA, Words and Models, New York, Cambridge Univ. Press
-
S. Robin, F. Rodolphe, and S. Schbath, DNA, Words and Models, New York, Cambridge Univ. Press, 2005.
-
(2005)
-
-
Robin, S.1
Rodolphe, F.2
Schbath, S.3
-
38
-
-
0002294347
-
A simple sequentially rejective multiple test procedure,
-
2
-
S. Holm, A simple sequentially rejective multiple test procedure, Scand J Stat 6(2) (1979), 65-70.
-
(1979)
Scand J Stat
, vol.6
, pp. 65-70
-
-
Holm, S.1
-
39
-
-
84857183849
-
Multiple hypothesis testing in pattern discovery,
-
and
-
S. Hanhijärvi, K. Puolamäki, and G. Garriga, Multiple hypothesis testing in pattern discovery, STAT 1050 (2009), 29.
-
(2009)
STAT
, vol.1050
, pp. 29
-
-
Hanhijärvi, S.1
Puolamäki, K.2
Garriga, G.3
-
40
-
-
0042424602
-
Statistical significance for genomewide studies,
-
16
-
J. Storey and R. Tibshirani, Statistical significance for genomewide studies, Proc Natl Acad Sci USA 100(16) (2003), 9440.
-
(2003)
Proc Natl Acad Sci USA
, vol.100
, pp. 9440
-
-
Storey, J.1
Tibshirani, R.2
-
41
-
-
85196088247
-
-
Multiple hypothesis testing in pattern discovery, Arxiv preprint arXiv:0906.5263
-
S. Hanhijärvi, K. Puolamäki, and G. Garriga, Multiple hypothesis testing in pattern discovery, Arxiv preprint arXiv:0906.5263, 2009.
-
(2009)
-
-
Hanhijärvi, S.1
Puolamäki, K.2
Garriga, G.3
-
42
-
-
0001677717
-
Controlling the false discovery rate: a practical and powerful approach to multiple testing,
-
1
-
Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J R Stat Soc B 57(1) (1995), 289-300.
-
(1995)
J R Stat Soc B
, vol.57
, pp. 289-300
-
-
Benjamini, Y.1
Hochberg, Y.2
-
43
-
-
85196105191
-
-
Statistical methods for data mining, Data Mining and Knowledge Discovery Handbook, Tel-Aviv, Israel, Springer
-
Y. Benjamini and M. Leshno, Statistical methods for data mining, Data Mining and Knowledge Discovery Handbook, Tel-Aviv, Israel, Springer, 2005, 565-587.
-
(2005)
, pp. 565-587
-
-
Benjamini, Y.1
Leshno, M.2
-
44
-
-
12244294068
-
-
On the discovery of significant statistical quantitative rules, In Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM
-
H. Zhang, B. Padmanabhan, and A. Tuzhilin, On the discovery of significant statistical quantitative rules, In Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2004, 374-383.
-
(2004)
, pp. 374-383
-
-
Zhang, H.1
Padmanabhan, B.2
Tuzhilin, A.3
-
45
-
-
62149117614
-
How accurate are the extremely small p-values used in genomic research: an evaluation of numerical libraries,
-
7
-
S. Santosh Bangalore, J. Wang, and D. Allison, How accurate are the extremely small p-values used in genomic research: an evaluation of numerical libraries, Comput Stat Data Anal 53(7) (2009), 2446-2452.
-
(2009)
Comput Stat Data Anal
, vol.53
, pp. 2446-2452
-
-
Santosh Bangalore, S.1
Wang, J.2
Allison, D.3
-
46
-
-
85196113276
-
-
HOT SAX: efficiently finding the most unusual time series subsequence, In Proceedings of the Fifth IEEE International Conference on Data Mining, IEEE Computer Society
-
E. Keogh, J. Lin, and A. Fu, HOT SAX: efficiently finding the most unusual time series subsequence, In Proceedings of the Fifth IEEE International Conference on Data Mining, IEEE Computer Society, 2005, 233.
-
(2005)
, pp. 233
-
-
Keogh, E.1
Lin, J.2
Fu, A.3
-
47
-
-
85196083614
-
-
Time series motifs statistical significance website.
-
N. Castro, Time series motifs statistical significance website.
-
-
-
Castro, N.1
-
48
-
-
2442693109
-
Fractal simulation of soil breakdown under lightning current,
-
3-4
-
Y. Gao, J. He, J. Zou, R. Zeng, and X. Liang, Fractal simulation of soil breakdown under lightning current, J Electrostat 61(3-4) (2004), 197-207.
-
(2004)
J Electrostat
, vol.61
, pp. 197-207
-
-
Gao, Y.1
He, J.2
Zou, J.3
Zeng, R.4
Liang, X.5
|