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: | Seok Bin Son, Soohyun Park, Joongheon Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10122955/ |
Similar Items
-
A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous Clusters
by: Long Chen, et al.
Published: (2014-10-01) -
Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering
by: Jérémie Sublime, et al.
Published: (2019-09-01) -
Entropy-based Consensus for Distributed Data Clustering
by: M. Owhadi-Kareshki, et al.
Published: (2019-11-01) -
Entropy clustering-based granular classifiers for network intrusion detection
by: Hui Liu, et al.
Published: (2020-01-01) -
Renyi entropy driven hierarchical graph clustering
by: Frédérique Oggier, et al.
Published: (2021-02-01)