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Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014 pp 99–111Cite as

An Efficient Approach of Multi-Relational Data Mining and Statistical Technique

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 327))

Abstract

The objective of data mining is to find the useful information from the huge amounts of data. Many researchers have been proposed the different algorithms to find the useful patterns but one of the most important drawbacks they have found that data mining techniques works for single data table. This technique is known as traditional data mining technique. In this era almost all data available in the form of relational database which have multiple tables and their relationships. The new data mining technique has emerged as an alternative for describing structured data such as relational data base, since they allow applying data mining in multiple tables directly, which is known as Multi Relational data mining. To avoid the more number joining operations as well as the semantic losses the researchers bound to use Multi Relational Data Mining approaches. In this paper MRDM focuses multi relational association rule, Multi relational decision tree construction, Inductive logic program (ILP) as well three statistical approaches. We emphasize each MR-Classification approach as well as their characteristics, comparisons as per the statistical values and finally found the most research challenging problems in MRDM.

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Authors and Affiliations

  1. Department of Computer Science, Gandhi Institute of Engineering and Technology, Gunupur, 765022, Odisha, India

    Neelamadhab Padhy & Rasmita Panigrahi

Authors
  1. Neelamadhab Padhy
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  2. Rasmita Panigrahi
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Correspondence to Neelamadhab Padhy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Bhubaneswar Engineering College, Bhubaneswar, Odisha, India

    Bhabendra Narayan Biswal

  3. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K. Udgata

  4. Department of Computer Science and Engineering, University of Kalyanai, Faculty of Engg., Tech. & Management, Kalyanai, West Bengal, India

    J.K. Mandal

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Padhy, N., Panigrahi, R. (2015). An Efficient Approach of Multi-Relational Data Mining and Statistical Technique. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-11933-5_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11932-8

  • Online ISBN: 978-3-319-11933-5

  • eBook Packages: EngineeringEngineering (R0)

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