TY - JOUR
T1 - Fuzzy-Rough Intrigued Harmonic Discrepancy Clustering
AU - Yue, Guanli
AU - Qu, Yanpeng
AU - Yang, Longzhi
AU - Shang, Changjing
AU - Deng, Ansheng
AU - Chao, Fei
AU - Shen, Qiang
N1 - Funding information: Research funded by Dalian High-Level Talent Innovation Program (2018RQ70). Sˆer Cymru II COFUND Fellowship, UK.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates comparable performance for knowledge exploitation under the circumstance of information incomplete- ness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters which are challenging to be determined for different data types. This paper proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modelling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.
AB - Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates comparable performance for knowledge exploitation under the circumstance of information incomplete- ness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters which are challenging to be determined for different data types. This paper proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modelling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.
KW - Rough set
KW - Fuzzy-rough set
KW - Clustering
KW - Harmonic discrepancy
UR - http://www.scopus.com/inward/record.url?scp=85149383290&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2023.3247912
DO - 10.1109/TFUZZ.2023.3247912
M3 - Article
AN - SCOPUS:85149383290
SN - 1063-6706
VL - 31
SP - 3305
EP - 3318
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 10
ER -