Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy
Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k- medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the b...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article PeerReviewed |
Language: | English |
Published: |
International Information and Engineering Technology Association (IIETA)
2022
|
Subjects: | |
Online Access: | https://repository.ugm.ac.id/282735/1/Kariyam_PA.pdf https://repository.ugm.ac.id/282735/ http://iieta.org/journals/mmep https://doi.org/10.18280/mmep.090622 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universitas Gadjah Mada |
Language: | English |
id |
id-ugm-repo.282735 |
---|---|
record_format |
dspace |
spelling |
id-ugm-repo.2827352023-11-16T07:01:43Z https://repository.ugm.ac.id/282735/ Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy Kariyam, Kariyam Abdurakhman, Abdurakhman Subanar, Subanar Utami, Herni Effendie, Adhitya Ronnie Applied Mathematics Mathematics and Applied Sciences Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k- medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the block of the standard deviation and the sum of the variable values. We optimized the initial groups to update medoids, so this step can reduce the number of iterations to obtain partitioned data. The block-based k- medoids partitioning method applies to all types of data. To improve clustering accuracy, we operate pre-processing through data standardization. We conducted a series of experiments on eight real data sets and three artificial data to evaluate the proposed method's performance in terms of clustering accuracy. The experiment results show that the Block-based K-Medoids partitioning is more efficient in reducing the number of iterations. The clustering accuracy of the Block-KM for eight real datasets is also comparable to other methods. The data standardization is effective to increase clustering accuracy, especially for block k-medoids, k-means, simple and fast k-medoids, and the Ward method. International Information and Engineering Technology Association (IIETA) 2022 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/282735/1/Kariyam_PA.pdf Kariyam, Kariyam and Abdurakhman, Abdurakhman and Subanar, Subanar and Utami, Herni and Effendie, Adhitya Ronnie (2022) Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy. Mathematical Modelling of Engineering Problem, 9 (6). pp. 1613-1621. ISSN 2369-0747 http://iieta.org/journals/mmep https://doi.org/10.18280/mmep.090622 |
institution |
Universitas Gadjah Mada |
building |
UGM Library |
continent |
Asia |
country |
Indonesia Indonesia |
content_provider |
UGM Library |
collection |
Repository Civitas UGM |
language |
English |
topic |
Applied Mathematics Mathematics and Applied Sciences |
spellingShingle |
Applied Mathematics Mathematics and Applied Sciences Kariyam, Kariyam Abdurakhman, Abdurakhman Subanar, Subanar Utami, Herni Effendie, Adhitya Ronnie Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy |
description |
Most of the existing k-medoid algorithms select the initial medoid randomly or use a specific formula based on the proximity matrix. This study proposes a block-based k-
medoids partitioning method for clustering objects. To get the initial medoids, we search for an object representative from the block of the standard deviation and the sum
of the variable values. We optimized the initial groups to update medoids, so this step can reduce the number of iterations to obtain partitioned data. The block-based k-
medoids partitioning method applies to all types of data. To improve clustering accuracy, we operate pre-processing through data standardization. We conducted a series of experiments on eight real data sets and three artificial data to evaluate the proposed method's performance in terms of clustering accuracy. The experiment results show that the Block-based K-Medoids partitioning is more efficient in reducing the number of iterations. The clustering accuracy of the Block-KM for eight real datasets is also comparable to other methods. The data standardization is effective to increase clustering accuracy, especially for block k-medoids, k-means, simple and fast k-medoids, and the Ward method. |
format |
Article PeerReviewed |
author |
Kariyam, Kariyam Abdurakhman, Abdurakhman Subanar, Subanar Utami, Herni Effendie, Adhitya Ronnie |
author_facet |
Kariyam, Kariyam Abdurakhman, Abdurakhman Subanar, Subanar Utami, Herni Effendie, Adhitya Ronnie |
author_sort |
Kariyam, Kariyam |
title |
Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy |
title_short |
Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy |
title_full |
Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy |
title_fullStr |
Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy |
title_full_unstemmed |
Block-Based K-Medoids Partitioning Method with Standardized Data to Improve Clustering Accuracy |
title_sort |
block-based k-medoids partitioning method with standardized data to improve clustering accuracy |
publisher |
International Information and Engineering Technology Association (IIETA) |
publishDate |
2022 |
url |
https://repository.ugm.ac.id/282735/1/Kariyam_PA.pdf https://repository.ugm.ac.id/282735/ http://iieta.org/journals/mmep https://doi.org/10.18280/mmep.090622 |
_version_ |
1783956332473221120 |