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Discovering Periodic-Correlated Patterns in Temporal Databases

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 11250))

Abstract

The support and periodicity are two important dimensions to determine the interestingness of a pattern in a dataset. Periodic-frequent patterns are an important class of regularities that exist in a dataset with respect to these two dimensions. Most previous models on periodic-frequent pattern mining have focused on finding all patterns in a transactional database that satisfy the user-specified minimum support (minSup) and maximum periodicity (maxPer) constraints. These models suffer from the following two obstacles: (i) Current periodic-frequent pattern models cannot handle datasets in which multiple transactions can share a common time stamp and/or transactions occur at irregular time intervals (ii) The usage of single minSup and maxPer for finding the patterns leads to the rare item problem. This paper tries to address these two obstacles by proposing a novel model to discover periodic-correlated patterns in a temporal database. Considering the input data as a temporal database addresses the first obstacle, while finding periodic-correlated patterns address the second obstacle. The proposed model employs all-confidence measure to prune the uninteresting patterns in support dimension. A new measure, called periodic-all-confidence, is being proposed to filter out uninteresting patterns in periodicity dimension. A pattern-growth algorithm has also been discussed to find periodic-correlated patterns. Experimental results show that the proposed model is efficient.

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Notes

  1. 1.

    A set of items represents a pattern (or an itemset).

  2. 2.

    Classifying the items into either frequent or rare is a subjective issue that depends upon the user and/or application requirements.

  3. 3.

    The term ‘pattern’ in a time series represents a set of itemsets (or sets of items).

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Acknowledgements

This research was partly supported by Real World Information Analytics project of National Institute of Information and Communications Technology, Japan.

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Authors and Affiliations

  1. Kohli Center on Intelligent Systems (KCIS), International Institute of Information Technology Hyderabad, Hyderabad, India

    J. N. Venkatesh & P. Krishna Reddy

  2. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    R. Uday Kiran & Masaru Kitsuregawa

  3. National Institute of Information and Communication Technologies, Tokyo, Japan

    R. Uday Kiran

  4. National Institute of Informatics, Tokyo, Japan

    Masaru Kitsuregawa

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  1. J. N. Venkatesh
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  4. Masaru Kitsuregawa
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Correspondence to J. N. Venkatesh .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Roland Wagner

  3. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  4. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

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Venkatesh, J.N., Uday Kiran, R., Krishna Reddy, P., Kitsuregawa, M. (2018). Discovering Periodic-Correlated Patterns in Temporal Databases. In: Hameurlain, A., Wagner, R., Hartmann, S., Ma, H. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII. Lecture Notes in Computer Science(), vol 11250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58384-5_6

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