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Ability Study of Proximity Measure for Big Data Mining Context on Clustering

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Proceedings of International Conference on Communication and Computational Technologies

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Proximity measure is used for data mining such as classification, cluster construction, regression, statistical analysis, analyzed and validated the mine results, and so on. Due to clustering, the proximity measure is based on cluster construction and cluster validation. Nowadays, growing of digital and communication technology is changing the nature of traditional data to big data. When any traditional taxonomy used for big data mining, it suffers various challenges due to big data dimensions. Proximity measure is one of the challenging issues of the clustering under big data criteria such as Volume, Variety, and Velocity. From a theoretically, practically and the existing research perspective, this paper study various proximity measure under Minkowski, L(1), L(2), Inner product, Shannon’s entropy, Combination, Intersection, and Fidelity family and recognized proximity measures for Volume (Dataset size), Variety (Data type), and Velocity (Time complexity) big data criteria. This study also identifies how to use these proximity measures for cluster construction under the Partition, Hierarchical, Density, Grid, Model, Fuzzy, and Graph-based clustering.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Applications, Dr. Hari Singh Gour Vishwavidyalaya, Sagar, MP, India

    Kamlesh Kumar Pandey & Diwakar Shukla

Authors
  1. Kamlesh Kumar Pandey
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  2. Diwakar Shukla
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Corresponding author

Correspondence to Kamlesh Kumar Pandey .

Editor information

Editors and Affiliations

  1. Rajasthan Technical University, Kota, Rajasthan, India

    Sunil Dutt Purohit

  2. Department of Computer Science, University of Science and Technology, Namibia, Windhoek, Namibia

    Dharm Singh Jat

  3. Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Alesund, Norway

    Ramesh Chandra Poonia

  4. Department of Computer Science, Amity University, Jaipur, Rajasthan, India

    Sandeep Kumar

  5. Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India

    Saroj Hiranwal

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Pandey, K.K., Shukla, D. (2021). Ability Study of Proximity Measure for Big Data Mining Context on Clustering. In: Purohit, S., Singh Jat, D., Poonia, R., Kumar, S., Hiranwal, S. (eds) Proceedings of International Conference on Communication and Computational Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5077-5_1

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