Abstract
A large class of problems deals with temporal data. Identifying temporal patterns in these datasets is a natural as well as an important task. In the recent time, 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 paper, we have extended the concept of certainty factor by incorporating support information for effective analysis of overlapped intervals. We have proposed a number of improvements in 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 our 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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Adhikari, J., Rao, P.R. (2013). Identifying Calendar-Based Periodic Patterns. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_13
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DOI: https://doi.org/10.1007/978-3-642-28699-5_13
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