Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection
Clustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups. It is a crucial yet demanding subject in machine learning and data mining. Its successful applications span various fields. However, conventional clustering techniques necessitate the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10122955/ |
| _version_ | 1797805942543220736 |
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| author | Seok Bin Son Soohyun Park Joongheon Kim |
| author_facet | Seok Bin Son Soohyun Park Joongheon Kim |
| author_sort | Seok Bin Son |
| collection | DOAJ |
| description | Clustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups. It is a crucial yet demanding subject in machine learning and data mining. Its successful applications span various fields. However, conventional clustering techniques necessitate the consideration of balance significance in specific applications. Therefore, this paper addresses the challenge of imbalanced clustering problems and presents a new method for balanced clustering by utilizing entropy-aware similarity, which can be defined as the degree of balances. We have coined the term, entropy-aware similarity for balanced clustering (EASB), which maximizes balance during clustering by complementary clustering of unbalanced data and incorporating entropy in a novel similarity formula that accounts for both angular differences and distances. The effectiveness of the proposed approach is evaluated on actual melanoma medial data, specifically the International Skin Imaging Collaboration (ISIC) 2019 and 2020 challenge datasets, to demonstrate how it can successfully cluster the data while preserving balance. Lastly, we can confirm that the proposed method exhibited outstanding performance in detecting melanoma, comparing to classical methods. |
| first_indexed | 2024-03-13T05:59:51Z |
| format | Article |
| id | doaj.art-256a3c5ee97c40cfbb9effd485db3782 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T05:59:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-256a3c5ee97c40cfbb9effd485db37822023-06-12T23:01:16ZengIEEEIEEE Access2169-35362023-01-0111468924690210.1109/ACCESS.2023.327556110122955Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma DetectionSeok Bin Son0https://orcid.org/0000-0002-3692-0752Soohyun Park1https://orcid.org/0000-0002-6556-9746Joongheon Kim2https://orcid.org/0000-0003-2126-768XDepartment of Electrical and Computer Engineering, Korea University, Seoul, Republic of KoreaDepartment of Electrical and Computer Engineering, Korea University, Seoul, Republic of KoreaDepartment of Electrical and Computer Engineering, Korea University, Seoul, Republic of KoreaClustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups. It is a crucial yet demanding subject in machine learning and data mining. Its successful applications span various fields. However, conventional clustering techniques necessitate the consideration of balance significance in specific applications. Therefore, this paper addresses the challenge of imbalanced clustering problems and presents a new method for balanced clustering by utilizing entropy-aware similarity, which can be defined as the degree of balances. We have coined the term, entropy-aware similarity for balanced clustering (EASB), which maximizes balance during clustering by complementary clustering of unbalanced data and incorporating entropy in a novel similarity formula that accounts for both angular differences and distances. The effectiveness of the proposed approach is evaluated on actual melanoma medial data, specifically the International Skin Imaging Collaboration (ISIC) 2019 and 2020 challenge datasets, to demonstrate how it can successfully cluster the data while preserving balance. Lastly, we can confirm that the proposed method exhibited outstanding performance in detecting melanoma, comparing to classical methods.https://ieeexplore.ieee.org/document/10122955/Balanced clusteringclusterentropymelanoma detection |
| spellingShingle | Seok Bin Son Soohyun Park Joongheon Kim Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection IEEE Access Balanced clustering cluster entropy melanoma detection |
| title | Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection |
| title_full | Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection |
| title_fullStr | Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection |
| title_full_unstemmed | Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection |
| title_short | Entropy-Aware Similarity for Balanced Clustering: A Case Study With Melanoma Detection |
| title_sort | entropy aware similarity for balanced clustering a case study with melanoma detection |
| topic | Balanced clustering cluster entropy melanoma detection |
| url | https://ieeexplore.ieee.org/document/10122955/ |
| work_keys_str_mv | AT seokbinson entropyawaresimilarityforbalancedclusteringacasestudywithmelanomadetection AT soohyunpark entropyawaresimilarityforbalancedclusteringacasestudywithmelanomadetection AT joongheonkim entropyawaresimilarityforbalancedclusteringacasestudywithmelanomadetection |