Name
Abstract | ||
|---|---|---|
Maximum Entropy Clustering (MEC) is an algorithm based on fuzzy c means by embedding an entropy generalization term in it. However, MEC is not robust to both noise and outliers, which leads to poor accuracy in clustering processes. In this paper, a novel clustering algorithm based on Shannon entropy is proposed, the new algorithm named Anti-noise Possibilistic Maximum Entropy Clustering (A-PMEC) is verified much more robustness in noisy dataset. We introduce the detailed formulation of A-PMEC and as well as experimental study to demonstrate the merits of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISKE.2017.8258729 | 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) |
Keywords | Field | DocType |
Fuzzy Clustering,Entropy,Possibilistic C-Means | Embedding,Computer science,Fuzzy logic,Algorithm,Robustness (computer science),Linear programming,Principle of maximum entropy,Cluster analysis,Entropy (information theory),Principal component analysis | Conference |
ISBN | Citations | PageRank |
978-1-5386-1830-1 | 0 | 0.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingguang Pan | 1 | 3 | 1.16 |
| Xiongtao Zhang | 2 | 8 | 2.59 |
| Z. B. Jiang | 3 | 242 | 36.08 |
| Shitong Wang | 4 | 1485 | 109.13 |