Rights statement: ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Accepted author manuscript, 1.07 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Novel Data-driven Approach to Autonomous Fuzzy Clustering
AU - Gu, X.
AU - Ni, Q.
AU - Tang, G.
N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - In this paper, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC firstly uses all the data samples as micro-cluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a one pass manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles.
AB - In this paper, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC firstly uses all the data samples as micro-cluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a one pass manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles.
KW - Clustering algorithms
KW - Data mining
KW - Data models
KW - data-driven
KW - fuzzy clustering
KW - Kernel
KW - Linear programming
KW - locally optimal partition
KW - medoids
KW - Nickel
KW - Partitioning algorithms
KW - pattern recognition
KW - Benchmarking
KW - Fuzzy clustering
KW - Iterative methods
KW - Membership functions
KW - Bench-mark problems
KW - Data distribution
KW - Data-driven approach
KW - Membership matrix
KW - Micro-clusters
KW - On-line applications
KW - Optimal partitions
KW - Streaming data
U2 - 10.1109/TFUZZ.2021.3074299
DO - 10.1109/TFUZZ.2021.3074299
M3 - Journal article
VL - 30
SP - 2073
EP - 2085
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
SN - 1063-6706
IS - 6
ER -