Abstract
A large class of problems is concerned with temporal data. Identifying temporal patterns in these datasets is a fully justifiable as well as an important task. Recently, researchers have reported an algorithm for finding calendar-based periodic pattern in a time-stamped data and introduced the concept of certainty factor in association with an overlapped interval. In this chapter, we have extended the concept of certainty factor by incorporating support information for effective analysis of overlapping intervals. We have proposed a number of improvements of the algorithm for identifying calendar-based periodic patterns. In this direction we have proposed a hash based data structure for storing and managing patterns. Based on this modified algorithm, we identify full as well as partial periodic calendar-based patterns. We provide a detailed data analysis incorporating various parameters of the algorithm and make a comparative analysis with the existing algorithm, and show the effectiveness of our algorithm. Experimental results are provided on both real and synthetic databases.
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Notes
- 1.
Frequent itemset mining dataset repository, http://fimi.cs.helsinki.fi/data.
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Adhikari, A., Adhikari, J. (2015). Mining Calendar-Based Periodic Patterns in Time-Stamped Data. In: Advances in Knowledge Discovery in Databases. Intelligent Systems Reference Library, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-319-13212-9_13
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DOI: https://doi.org/10.1007/978-3-319-13212-9_13
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