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Data mining with Temporal Abstractions: learning rules from time series

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Abstract

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.

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References

  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the international conference on very large databases. Santiago, Chile, pp 487–499

  • Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th international conference on data engineering. Taipei, Taiwan, pp 3–14

  • Allen JF (1984). Towards a general theory of action and time. Artif Intell 23: 123–154

    Article  MATH  Google Scholar 

  • Bellazzi R, Larizza C and Riva A (1998). Temporal abstractions for interpreting diabetic patients monitoring data. Intell Data Anal 2: 97–122

    Article  Google Scholar 

  • Bellazzi R, Larizza C, Magni P, Bellazzi R (2003) Quality assessment of hemodialysis services through temporal data mining. In: Proceedings of 9th conference on artificial intelligence in medicine (AIME). Protaras, Cyprus, pp 11–20

  • Bellazzi R, Larizza C, Magni P and Bellazzi R (2005). Temporal data mining for the quality assessment of hemodialysis services. Artif Intell Med 34(1): 25–39

    Article  Google Scholar 

  • Brown PO and Botstein D (1999). Exploring the new world of the genome with DNA microarrays. Nat Genet 21(1 Suppl): 33–37

    Article  Google Scholar 

  • Chen X, Petrounias I, Heathfield H (1998) Discovering temporal association rules in temporal databases. In: Proceedings of the international workshop on issues and applications of database technology (IADT’98). Berlin, Germany, pp 312–319

  • Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: Proceedings of the 9th ACM SIGKDD international confernence on knowledge discovery and data mining. Washington, USA, pp 493–498

  • Cohen PR (2001): Fluent learning: elucidating the structure of episodes. In: Hoffmann F, Hand D, Adams N, Fisher D, Guimaraes G (eds) Proceedings of the 4th international conf on Intelligent data analysis (IDA 2001). Cascais, Portugal, pp: 268–277

  • Combi C and Chittaro L (1999). Abstraction on clinical data sequences: an object-oriented data model and a query language based on the event calculus. Artif Intell Med 17(3): 271–301

    Article  Google Scholar 

  • Guimarães G, Ultsch A (1999) A method for temporal knowledge conversion. In: Proceedings of the 3rd international symposium on advances in intelligent data analysis (IDA 1999). Amsterdam, The Netherlands, pp 369–382

  • Guimarães G, Peter JH, Penzel T and Ultsch A (2001). A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders. Artif Intell Med 23(3): 211–37

    Article  Google Scholar 

  • Haimowitz IJ, Kohane IS (1993) Automated trend detection with alternate temporal hypotheses. In: Proceedings of the 13th joint conference on artificial intelligence (IJCAI 1993). Chambery, France, pp 146–151

  • Hau DT and Coiera EW (1997). Learning qualitative models of dynamic systems. Mach Learn 26(2–3): 177–211

    Article  MATH  Google Scholar 

  • Höppner F, Klawonn F (2002a) Learning rules about the development of variables over time. In: Leondes CT (ed), Intelligent systems—techniques and applications, Vol 4. CRC Press, pp 201–228

  • Höppner F and Klawonn F (2002b). Finding informative rules in interval sequences. Intell Data Anal Int J 6(3): 237–256

    MATH  Google Scholar 

  • Höppner F (2003) Knowledge discovery from sequential data. PhD thesis, Technical University Braunschweig, Germany

  • Kam PS, Fu AWC (2000) Discovering temporal patterns for interval-based events. In: Proceedings of 2nd international conference on data warehousing and knowledge discovery (DaWaK). London, UK, pp 317–326

  • Keogh E, Chu S, Hart D, Pazzani M (2004) Segmenting time series: a survey and novel approach. Data mining in time series databases, World Scientific Publishing Company, pp 1–22

  • Kuipers B (1986). Qualitative simulation. Artif Intell 29(3): 289–338

    Article  MATH  Google Scholar 

  • Larizza C, Moglia A and Stefanelli M (1992). M-HTP: a system for monitoring heart transplant patients. Artif Intell Med 4(2): 111–126

    Article  Google Scholar 

  • Last M, Klein Y and Kandel A (2001). Knowledge discovery in time series databases. IEEE Trans Syst Man Cybernet 31(1): 160–169

    Article  Google Scholar 

  • Li Y, Ning P, Wang XS and Jajodia S (2003). Discovering calendar-based temporal association rules. Data Knowl Eng 44(2): 193–218

    Article  Google Scholar 

  • Lin W, Orgun MA, Williams GJ (2002) An Overview of Temporal Data Mining. In: Proceedings of the 1st Australian data mining workshop (ADM02). Canberra, Australia, pp 83–90

  • Lin M-Y and Lee S-Y (2005). Fast discovery of sequential patterns through memory indexing and database partitioning. J Inf Sci Eng 21(1): 109–128

    Google Scholar 

  • Mannila H, Toivonen H (1996) Discovering generalized episodes using minimal occurrences. In: Proceedings of the 2nd international conference on knowledge discovery and data mining (KDD-96). Portland, Oregon, pp 146–151

  • Mörchen F (2006) Time series knowledge mining. PhD thesis, Philipps University, Marburg, Germany

  • Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2005) Discovering frequent arrangements of temporal intervals. In: Proceedings of the 5th IEEE international conference on data mining (ICDM-05). Houston, Texas, pp 354–361

  • Roddick JF and Spiliopoulou M (2002). A survey of temporal knowledge discovery paradigms and methods. IEEE Trans Knowledge Data Eng 14(4): 750–767

    Article  Google Scholar 

  • Sacchi L, Bellazzi R, Larizza C, Magni P, Curk T, Petrovic U and Zupan B (2005a). TA-clustering: cluster analysis of gene expression profiles through Temporal Abstractions. Int J Med Inform 74(7–8): 505–517

    Article  Google Scholar 

  • Sacchi L, Bellazzi R, Porreca R, Larizza C, Magni P (2005b) Precedence Temporal Networks from gene expression data. In: Proceedings of 18th IEEE symposium on computer-based medical systems (CBMS). Dublin, Ireland, pp 109–114

  • Shahar Y and Musen MA (1996). Knowledge-based temporal abstraction in clinical domains. Artif Intell Med 8(3): 267–298

    Article  Google Scholar 

  • Shahar Y (1997). A framework for knowledge-based temporal abstraction. Artif Intell 90(1–2): 79–133

    Article  MATH  Google Scholar 

  • Tung AKH, Lu H, Han J and Feng L (2003). Efficient mining of intertransaction association rules. IEEE Trans Knowl Data Eng 15(1): 43–56

    Article  Google Scholar 

  • Tyson JJ, Chen K and Novak B (2001). Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2(12): 908–916

    Article  Google Scholar 

  • Villafane R, Hua KA, Tran D and Maulik B (2000). Knowledge discovery from series of interval events. J Intell Inform Syst 15(1): 71–89

    Article  Google Scholar 

  • Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, Alexander KE, Matese JC, Perou CM, Hurt MM, Brown PO and Botstein D (2002). Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13(6): 1977–2000

    Article  Google Scholar 

  • Winarko E, Roddick JF (2005) Discovering richer temporal association rules from interval-based data. In: Proceedings of the international conference on data warehousing and knowledge discovery (DaWaK). Copenhagen, Denmark, pp 315–325

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Authors and Affiliations

  1. Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy

    Lucia Sacchi, Cristiana Larizza & Riccardo Bellazzi

  2. Dipartimento di Informatica, Università degli Studi di Verona, Verona, Italy

    Carlo Combi

Authors
  1. Lucia Sacchi
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  2. Cristiana Larizza
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  3. Carlo Combi
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  4. Riccardo Bellazzi
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Correspondence to Lucia Sacchi.

Additional information

Communicated by: Eamonn Keogh.

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Sacchi, L., Larizza, C., Combi, C. et al. Data mining with Temporal Abstractions: learning rules from time series. Data Min Knowl Disc 15, 217–247 (2007). https://doi.org/10.1007/s10618-007-0077-7

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  • DOI: https://doi.org/10.1007/s10618-007-0077-7

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