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Optimal Number of Seed Point Selection Algorithm of Unknown Dataset

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Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1024))

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Abstract

In the present world, clustering is considered to be the most important data mining tool which is applied to huge data to help the futuristic decision-making processes. It is an unsupervised classification technique by which the data points are grouped to form the homogeneous entity. Cluster analysis is used to find out the clusters from a unlabeled data. The position of the seed points primarily affects the performances of most partitional clustering techniques. The correct number of clusters in a dataset plays an important role to judge the quality of the partitional clustering technique. Selection of initial seed of K-means clustering is a critical problem for the formation of the optimal number of the cluster with the benefit of fast stability. In this paper, we have described the optimal number of seed points selection algorithm of an unknown data based on two important internal cluster validity indices, namely, Dunn Index and Silhouette Index. Here, Shannon’s entropy with the threshold value of distance has been used to calculate the position of the seed point. The algorithm is applied to different datasets and the results are comparatively better than other methods. Moreover, the comparisons have been done with other algorithms in terms of different parameters to distinguish the novelty of our proposed method.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), IIT(ISM), Dhanbad, 826004, Jharkhand, India

    Kuntal Chowdhury & Arup Kumar Pal

  2. DRDO Integration Centre, Panagarh, West Bengal, India

    Debasis Chaudhuri

Authors
  1. Kuntal Chowdhury
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  2. Debasis Chaudhuri
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  3. Arup Kumar Pal
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Corresponding author

Correspondence to Kuntal Chowdhury .

Editor information

Editors and Affiliations

  1. Techno India University, Kolkata, India

    Bidyut B. Chaudhuri

  2. Division of Advanced Information Technology and Computer Science, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Chowdhury, K., Chaudhuri, D., Pal, A.K. (2020). Optimal Number of Seed Point Selection Algorithm of Unknown Dataset. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_21

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  • DOI: https://doi.org/10.1007/978-981-32-9291-8_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9290-1

  • Online ISBN: 978-981-32-9291-8

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