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Computational Intelligence in Pattern Recognition pp 399–411Cite as

Empirical Analysis of Proximity Measures in Machine Learning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 999))

Abstract

Availability of abundant and various types of proximity measures often projects a challenge in both supervised and unsupervised learning processes. There are various similarity and dissimilarity measures proposed in the literature of machine learning. These measures differ with respect to various issues imposed by different application domains such as ability to handle noise, ability to detect various types of correlation, and coping with large number of dimensions. In this work, we pick-up eighteen proximity measures and apply them on two well known distance-based learning frameworks. One framework uses a widely used supervised learning method, i.e., KNN classifier and the other uses an unsupervised learning method called k-means clustering.

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Author information

Authors and Affiliations

  1. Department of CSE, Tezpur University, Sonitpur, 784028, Assam, India

    Nazrul Hoque & Dhruba Kumar Bhattacharyya

  2. Department of Information and Computer Science, Assam Women’s University, Jorhat, 785004, Assam, India

    Hasin A. Ahmed

Authors
  1. Nazrul Hoque
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  2. Hasin A. Ahmed
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  3. Dhruba Kumar Bhattacharyya
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Corresponding author

Correspondence to Nazrul Hoque .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India

    Asit Kumar Das

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Soumen Kumar Pati

  5. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Hoque, N., Ahmed, H.A., Bhattacharyya, D.K. (2020). Empirical Analysis of Proximity Measures in Machine Learning. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_34

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