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An Effective Approach for Mining Weighted Sequential Patterns

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Smart Trends in Information Technology and Computer Communications (SmartCom 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 628))

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Abstract

Sequential pattern mining is one of the most studied data mining problem and has wide range of application domains including weather prediction, network intrusion detection, web access analysis, customer purchase analysis, etc. The weighted sequential pattern mining is an approach to find only interesting sequential patterns by assigning weights to data elements present in the sequences. The time-interval weighted sequential pattern mining is another approach in which different weights are assigned to the time-interval values between the successive transactions. From customer purchase pattern analysis point of view, both item’s importance as well as time-interval gap values is useful and more interesting patterns can be discovered by considering them while assigning weights to the sequences. This paper aims to propose a novel approach for finding weighted sequential patterns from customer retail database which incorporates both the item’s importance and time-interval gap information so that the discovered sequential patterns will be more meaningful and effective for the end-user. The results infer a lot of computation cost can be saved by focusing on few interesting patterns.

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Authors and Affiliations

  1. Sarvajanik College of Engineering and Technology, Surat, Gujarat, India

    Mukesh Patel

  2. Department of Computer Science and Applications, Indus University, Ahmedabad, Gujarat, India

    Nilesh Modi

  3. Department of Mathematics and Computer Science, Laurentian University, Sudbury, Canada

    Kalpdrum Passi

Authors
  1. Mukesh Patel
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  2. Nilesh Modi
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  3. Kalpdrum Passi
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Corresponding author

Correspondence to Mukesh Patel .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Aynur Unal

  2. IT Buzz Limited, Dagenham, UK

    Malaya Nayak

  3. Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  4. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  5. Sabar Institute of Technology, Sabarkantha, India

    Amit Joshi

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© 2016 Springer Nature Singapore Pte Ltd.

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Patel, M., Modi, N., Passi, K. (2016). An Effective Approach for Mining Weighted Sequential Patterns. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_108

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  • DOI: https://doi.org/10.1007/978-981-10-3433-6_108

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3432-9

  • Online ISBN: 978-981-10-3433-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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