Abstract
In this paper, two semi-supervised clustering methods are proposed, which are based on entropy regularized fuzzy c-means algorithm. First, two fuzzy c-means algorithms are introduced. The one is the standard one and the other is the entropy regularized one. Second, two semi-supervised standard fuzzy c-means algorithms are introduced, which are derived from adding loss function of memberships to the original optimization problem. Third, two new optimization problems are proposed, in which one is derived from adding new loss function of memberships to the original optimization problem and the other is derived from adding the loss function used in the latter semisupervised standard fuzzy c-means algorithm. Last, two iterative algorithms are proposed by solving the optimization problems.
| Original language | English |
|---|---|
| Title of host publication | SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems |
| Pages | 61-65 |
| Number of pages | 5 |
| Publication status | Published - 2010 |
| Event | Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama Duration: 2010 Dec 8 → 2010 Dec 12 |
Other
| Other | Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 |
|---|---|
| City | Okayama |
| Period | 10/12/8 → 10/12/12 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
Cite this
Two semi-supervised entropy regularized fuzzy c-means. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.
SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. 2010. p. 61-65.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Two semi-supervised entropy regularized fuzzy c-means
AU - Kanzawa, Yuchi
AU - Endo, Yasunori
AU - Miyamoto, Sadaaki
PY - 2010
Y1 - 2010
N2 - In this paper, two semi-supervised clustering methods are proposed, which are based on entropy regularized fuzzy c-means algorithm. First, two fuzzy c-means algorithms are introduced. The one is the standard one and the other is the entropy regularized one. Second, two semi-supervised standard fuzzy c-means algorithms are introduced, which are derived from adding loss function of memberships to the original optimization problem. Third, two new optimization problems are proposed, in which one is derived from adding new loss function of memberships to the original optimization problem and the other is derived from adding the loss function used in the latter semisupervised standard fuzzy c-means algorithm. Last, two iterative algorithms are proposed by solving the optimization problems.
AB - In this paper, two semi-supervised clustering methods are proposed, which are based on entropy regularized fuzzy c-means algorithm. First, two fuzzy c-means algorithms are introduced. The one is the standard one and the other is the entropy regularized one. Second, two semi-supervised standard fuzzy c-means algorithms are introduced, which are derived from adding loss function of memberships to the original optimization problem. Third, two new optimization problems are proposed, in which one is derived from adding new loss function of memberships to the original optimization problem and the other is derived from adding the loss function used in the latter semisupervised standard fuzzy c-means algorithm. Last, two iterative algorithms are proposed by solving the optimization problems.
UR - http://www.scopus.com/inward/record.url?scp=84866648466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866648466&partnerID=8YFLogxK
M3 - Conference contribution
SP - 61
EP - 65
BT - SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems
ER -