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A Multi-objective Approach for Inter-cluster and Intra-cluster Distance Analysis for Numeric Data

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Soft Computing: Theories and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 425))

Abstract

A measure of how closely a set of data items are connected is determined based on the similarity or dissimilarity between the data items. Inter-cluster and intra-cluster distances are two metrics that exhibit how the data items can group accordingly to form clusters. Usually, the data items with high similarities are in the same cluster, and the distance between them is significantly less. A good clustering algorithm always maintains high similarity within the cluster, thereby maximizing the distance between the data items having higher dissimilarities in distinct clusters. These measures play a crucial role in identifying the patterns among the data objects. Euclidean distance acts as a distance measure for determining these two metrics. This paper proposes a multi-objective approach for clustering to establish the relationship between inter-cluster and intra-cluster distances. It aims to perform a comparative analysis of the sum of inter-cluster and intra-cluster distances. It reveals that when the sum of intra-cluster distance is minimizing, then the sum of inter-cluster distance is maximizing for a given dataset.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, India

    A. Binu Jose & Pranesh Das

Authors
  1. A. Binu Jose
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  2. Pranesh Das
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Corresponding author

Correspondence to A. Binu Jose .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Rajesh Kumar

  2. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Chang Wook Ahn

  3. Department of Computer Science, Shobhit University, Gangoh, India

    Tarun K. Sharma

  4. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Om Prakash Verma

  5. Indian Institute of Information Technology Kota, Jaipur, Rajasthan, India

    Anand Agarwal

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Binu Jose, A., Das, P. (2022). A Multi-objective Approach for Inter-cluster and Intra-cluster Distance Analysis for Numeric Data. In: Kumar, R., Ahn, C.W., Sharma, T.K., Verma, O.P., Agarwal, A. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-19-0707-4_30

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