  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.
