Classification of multivariate time series via temporal abstraction and time intervals mining

Knowledge and Information Systems

Volumn 45, Issue 1, 2015, Pages 35-74

  Moskovitch, Robert ^a,b   Shahar, Yuval ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b NewYork Presbyterian Hospital   (United States)

Author keywords

Classification; Frequent pattern mining; Temporal abstraction; Temporal knowledge discovery; Time intervals mining

Indexed keywords

CLASSIFICATION (OF INFORMATION); DISCRETE EVENT SIMULATION; FEATURE EXTRACTION; KNOWLEDGE BASED SYSTEMS;

FEATURE SELECTION METHODS; FREQUENT PATTERN MINING; MULTIVARIATE TIME SERIES; MULTIVARIATE TIME SERIES ANALYSIS; REPRESENTATION METHOD; SYMBOLIC AGGREGATE APPROXIMATION (SAX); TEMPORAL ABSTRACTION; TIME INTERVAL;

TIME SERIES ANALYSIS;

EID: 84942238379     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-014-0784-5     Document Type: Article

References (40)

1. Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843
2. Azulay R, Moskovitch R, Stopel D, Verduijn M, de Jonge E, Shahar Y (2007) Temporal discretization of medical time series—a comparative study. In: IDAMAP 2007, Amsterdam, The Netherlands,
3. Batal I, Valizadegan H, Cooper G, Hauskrecht M (2012a) A temporal pattern mining approach for classifying electronic health record data. ACM Transaction on Intelligent Systems and Technology (ACM TIST), Special Issue on Health Informatics
4. Batal I, Fradkin D, Harrison J, Moerchen F, Hauskrecht M (2012b) Mining recent temporal patterns for event detection in multivariate time series data. In: Proceedings of knowledge discovery and data mining (KDD), Beijing, China
5. Höppner F (2001) Learning temporal rules from state sequences. In: Proceedings of IJCAI Workshop on Learning from Temporal and Spatial Data (WLTSD-01), Seattle, USA, pp 25–31
6. Höppner F (2002) Time series abstraction methods—a survey workshop on knowledge discovery in databases, Dortmund
7. Hu B, Chen Y, Keogh E (2013) Time series classification under more realistic assumptions. In: Proceedings of SIAM data mining
8. Kam PS, Fu AWC (2000) Discovering temporal patterns for interval based events. In: Proceedings DaWaK-00
9. Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series with implications for streaming algorithms. In: 8th ACM SIGMOD DMKD workshop
10. Mörchen F, Ultsch A (2005) Optimizing time series discretization for knowledge discovery. In: Proceedings of the Eleventh ACM SIGKDD international conference on knowledge discovery in data mining, Chicago, Illinois, pp 660–665
11. Mörchen F (2006) Algorithms for time series knowledge mining. In: Proceedings of KDD
12. Moerchen F (2006) A better tool than Allen’s relations for expressing temporal knowledge in interval data. In: Workshop on temporal data mining
13. Moerchen F, Fradkin D (2010) Robust mining of time intervals with semi-interval partial order patterns. In: Proceedings of SIAM data mining
14. Moskovitch R, Hessing A, Shahar Y (2004) Vaidurya-a concept-based, context-sensitive search engine for clinical guidelines. Medinfo 11:140–144
15. Moskovitch R, Stopel D, Verduijn M, Peek N, de Jonge E, Shahar Y (2007) Analysis of ICU patients using the time series knowledge mining method. In: IDAMAP 2007, Amsterdam, The Netherlands
16. Moskovitch R, Gus I, Pluderman S, Stopel D, Glezer C, Shahar Y, Elovici Y (2007) Detection of unknown computer worms activity based on computer behavior using data mining. In: IEEE Symposiyum on Computational Intelligence and Data Mining, Honolulu, Hawaii
17. Moskovitch R, Shahar Y (2009) Vaidurya: a multiple-ontology, concept-based, context-sensitive clinical-guideline search engine. J Biomed Inform 42(1):11–21
18. Moskovitch R, Shahar Y (2009) Medical temporal-knowledge discovery via temporal abstraction. In: AMIA 2009, San Francisco, USA
19. Moskovitch R, Peek N, Shahar Y (2009) Classification of ICU patients via temporal abstraction and temporal patterns mining. In: IDAMAP, Verona, Italy
20. Moskovitch R, Shahar Y (2013) Fast time intervals mining using transitivity of temporal relations. Knowl Inf Syst. doi:10.1007/s10115-013-0707-x
21. Moskovitch R, Shahar Y (2014) Fast detection of time intervals related patterns, TechReport 11/14. Ben Gurion University, Beer Sheva, Israel
22. Moskovitch R, Walsh C, Hripcsak G, Tatonetti N (2014) Prediction of biomedical events via time intervals mining. In: Proceedings of ACM SIGKDD workshop on connected health at big data Era (BigCHat2014), New York, US
23. Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21(2):133–171
24. Patel D, Hsu W, Lee ML (2008) Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, pp 393–404
25. Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu MC (2001) PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th international conference data engineering (ICDE ’01)
26. Rabiner LR (1989) A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc IEEE 77(2):257–286
27. Ratanamahatana C, Keogh EJ (2005) Three myths about dynamic time warping data mining. In: Proceedings of SIAM data mining
28. Roddick J, Spiliopoulou M (2002) A survey of temporal knowledge discovery paradigms and methods. IEEE Trans Knowl Data Eng 4(14):750–767
29. Sacchi L, Larizza C, Combi C, Bellazi R (2007) Data mining with temporal abstractions: learning rules from time series. Data Mining Knowl Discov 15(2):217–247
30. Shahar Y (1997) A framework for knowledge-based temporal abstraction. Artif Intell 90(1–2):79–133
31. Shahar Y (1998) Dynamic temporal interpretation contexts for temporal abstraction. Ann Math Artif Intell 22(1–2):159–192
32. Shahar Y (1999) Knowledge-based temporal interpolation. J Exp Theor, Artif Intell 11:102–111
33. Shahar Y, Chen H, Stites D, Basso L, Kaizer H, Wilson D, Musen MA (1999) Semiautomated acquisition of clinical temporal-abstraction knowledge. J Am Med Inform Assoc 6(6):494–511
34. Shknevsky A, Moskovitch R, Shahar Y (2014) Semantic considerations in time intervals mining. In: Proceedings of ACM SIGKDD workshop on connected health at big data Era (BigCHat2014), New York, US
35. Stopel D, Boger Z, Moskovitch R, Shahar Y, Elovici Y (2006a) Application of artificial neural networks techniques to computer worm detection. In: International joint conference on neural networks, pp 2362–2369
36. Stopel D, Boger Z, Moskovitch R, Shahar Y, Elovici Y (2006b) Improving worm detection with artificial neural networks through feature selection and temporal analysis techniques. In: Proceedings of the third international conference on neural networks, Barcelona
37. Verduijn M, Sacchi L, Peek N, Bellazi R, de Jonge E, de Mol B (2007) Temporal abstraction for feature extraction: a comparative case study in prediction from intensive care monitoring data. Artif Intell Med 41:112
38. Villafane R, Hua K, Tran D, Maulik B (2000) Knowledge discovery from time series of interval events. J Intell Inf Syst 15(1):71–89
39. Winarko E, Roddick J (2007) Armada—an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng 1(63):76–90
40. Wu S, Chen Y (2007) Mining non-ambiguous temporal patterns for interval-based events. IEEE Trans Knowl Data Eng 19(6)742–758