Abstract
Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been considered. Such time series are common in many applications. In this paper, we propose mining techniques to discover temporal containment relationships in such series. Specifically, an item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships provides insight about temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.
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References
Agrawal, R., Faloutsos, C., and Swami, A. (1993). Efficient Similarity Search in Sequence Databases. Proceedings of the Conference of Foundations of Data Organization (pp. 69–84).
Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD International Conference on Management of Data (pp. 207–216).
Agrawal, R., Psaila, G., Wimmers, E.L., and Zait, M. (1995). Querying Shapes of Histories. International Conference on Very Large Data Bases (pp. 502–514).
Agrawal, R. and Srikant, R. (1995). Mining Sequential Patterns. International Conference on Data Engineering (pp. 3–14).
Bohlen, M.H., Busatto, R., and Jensen, C.S. (1998). Point-Versus Interval-based Temporal Data Models. International Conference Data Engineering (pp. 192–200).
Chakrabarti, S., Sarawagi, S., and Dom, B. (1998). Mining Surprising Patterns Using Temporal Description Length. International Conference on Very Large Data Bases (pp. 606–617).
Das, G., Lin, K., Mannila, H., Renganathan, G., and Smyth, P. (1998). Rule Discovery From Time Series. International Conference on Knowledge Discovery & Data Mining (pp. 16–22).
Mannila, H., Toivonen, H., and Verkamo, A.I. (1997). Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery, 1(3), 259–289.
Morimoto, Y., Ishii, H., and Morishita, S. (1997). Efficient Construction of Regression Trees with Range and Region Splitting. International Conference on Very Large Data Bases (pp. 166–175).
Rafiei, D. and Mendelzon, A. (1997). Similarity-Based Queries for Time Series Data. ACM SIGMOD International Conference on Management of Data (pp. 13–25).
Ramaswamy, S., Mahajan, S., and Silberschatz, A. (1998). On the Discovery of Interesting Patterns in Association Rules. International Conference on Very Large Data Bases (pp. 368–379).
Rastogi, R. and Shim, K. (1998). Mining Optimized Association Rules with Categorical and Numeric Attributes. International Conference on Data Engineering (pp. 503–512).
Shatkay, H. and Zdonik, S.B. (1996). Approximate Queries and Representations for Large Data Sequences. International Conference on Data Engineering (pp. 536–545).
Villafane, R., Hua, K.A., Tran, D., and Maulik, B. (1999). Mining Interval Time Series. International Conference on Data Warehousing and Knowledge Discovery (pp. 318–330).
Yazdani, N. and Ozsoyoglu, Z.M. (1996). Sequence Matching of Images. International Conference on Scientific and Statistical Data Base Management (pp. 53–62).
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Villafane, R., Hua, K.A., Tran, D. et al. Knowledge Discovery from Series of Interval Events. Journal of Intelligent Information Systems 15, 71–89 (2000). https://doi.org/10.1023/A:1008781812242
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DOI: https://doi.org/10.1023/A:1008781812242