Name
Abstract | ||
|---|---|---|
As a kind of important soft clustering model, the fuzzy C-means method is widely applied in many fields. In this method, instead of the strict distributive ability in the classical k-means method, all the sample points are endowed with degrees of membership to each center to depict the fuzzy clustering. In this paper, we show that the fuzzy C-means++ algorithm, which introduces the k-means++ algorithm as a seeding strategy, gives a solution for which the approximation guarantee is O(k(2) ln k). A novel seeding algorithm is then designed based on the contribution of the fuzzy potential function, which improves the approximation ratio to O(k lnk). Preliminary numerical experiments are proposed to support the theoretical results of this paper. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.tcs.2021.06.035 | THEORETICAL COMPUTER SCIENCE |
Keywords | DocType | Volume |
Fuzzy C-means problem, Seeding algorithm, Approximation algorithm, Approximation ratio | Journal | 885 |
ISSN | Citations | PageRank |
0304-3975 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Liu | 1 | 0 | 0.34 |
| Jianxin Liu | 2 | 0 | 1.35 |
| M. Li | 3 | 5 | 6.54 |
| Yang Zhou | 4 | 9 | 6.73 |