A Fuzzy K-modes-based Algorithm for Soft Subspace Clustering
- Resource Type
- Conference
- Authors
- Ji, Tengfei; Bao, Xiaoyuan; Wang, Yue; Yang, Dongqing
- Source
- 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on. 2:1080-1084 Jul, 2011
- Subject
- Signal Processing and Analysis
Components, Circuits, Devices and Systems
Communication, Networking and Broadcast Technologies
Computing and Processing
Clustering algorithms
Power capacitors
Machine learning algorithms
Optimization
Runtime
Size measurement
Soft Subspace Clustering
Mixed features
High-dimensional data
Fuzzy techniques
- Language
This paper proposes a Fuzzy K-modes-based Algorithm for Soft Subspace Clustering, which adopts some fuzzy techniques for subspace clustering on mixed features. In order to obtain better clustering result, the proposed algorithm focuses on not only the intra-similarity of clusters, but also the optimization of the subspace where the cluster is situated. Experimental results show that the proposed FKSSC algorithm is efficient and effective in clustering both categorical and numeral data sets in high dimensional space.