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International Conference on Soft Computing and Data Mining

SCDM 2022: Recent Advances in Soft Computing and Data Mining pp 14–24Cite as

PSS: New Parametric Based Clustering for Data Category

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 457)

Abstract

This paper proposes a new clustering technique for handling a categorical data called Parametric Soft set (PSS). It bases on statistical distribution namely multinomial multivariate function. The probability of the data category with binary value can be calculated by binomial distribution. Its generalization called multinomial distribution function for data category with multivariate values. Firstly, the data is represented as multi soft set where every object in each soft set has its probability. The probability of each object is calculated by cluster joint distribution function following the multivariate multinomial distribution function. The highest probability will be assigned to the related cluster. The first experiment is conducted to estimate the parameter of the data drawn from random multivariate mixtures distribution. While the second experiment is evaluated the processing times, purity and rand index using benchmarks datasets. The experiment results show that the proposed approach has improved the processing times up to 92.96%. It also has better performance in term of purity and rand index and error mean of the estimation parameters.

Keywords

  • Clustering
  • Categorical data
  • Multi soft set
  • Multinomial distribution function

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Author information

Authors and Affiliations

  1. Department of Information Systems, University of Ahmad Dahlan, Yogyakarta, Indonesia

    Iwan Tri Riyadi Yanto

  2. Faculty of Applied Science and Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Iwan Tri Riyadi Yanto & Norhalina Senan

Authors
  1. Iwan Tri Riyadi Yanto
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  2. Mustafa Mat Deris
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  3. Norhalina Senan
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Corresponding author

Correspondence to Iwan Tri Riyadi Yanto .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Nazri Mohd Nawi

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Prof. Dr. Mustafa Mat Deris

  4. School of Information Technology Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Prof. Jemal H. Abawajy

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Dr. Nureize Arbaiy

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Cite this paper

Yanto, I.T.R., Deris, M.M., Senan, N. (2022). PSS: New Parametric Based Clustering for Data Category. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_2

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