Conference proceeding
An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints
Proceedings of the 21th ACM SIGKDD International Conference on knowledge discovery and data mining, Vol.2015-, pp.1405-1414
KDD '15
08/10/2015
DOI: 10.1145/2783258.2783389
Abstract
Semi-supervised clustering leverages side information such as pairwise constraints to guide clustering procedures. Despite promising progress, existing semi-supervised clustering approaches overlook the condition of side information being generated sequentially, which is a natural setting arising in numerous real-world applications such as social network and e-commerce system analysis. Given emerged new constraints, classical semi-supervised clustering algorithms need to re-optimize their objectives over all data samples and constraints in availability, which prevents them from efficiently updating the obtained data partitions. To address this challenge, we propose an efficient dynamic semi-supervised clustering framework that casts the clustering problem into a search problem over a feasible convex set, i.e. , a convex hull with its extreme points being an ensemble of m data partitions. According to the principle of ensemble clustering, the optimal partition lies in the convex hull, and can thus be uniquely represented by an m -dimensional probability simplex vector. As such, the dynamic semi-supervised clustering problem is simplified to the problem of updating a probability simplex vector subject to the newly received pairwise constraints. We then develop a computationally efficient updating procedure to update the probability simplex vector in O ( m 2 ) time, irrespective of the data size n . Our empirical studies on several real-world benchmark datasets show that the proposed algorithm outperforms the state-of-the-art semi-supervised clustering algorithms with visible performance gain and significantly reduced running time.
Details
- Title: Subtitle
- An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints
- Creators
- Jinfeng Yi - IBM (United States)Lijun Zhang - Nanjing UniversityTianbao Yang - University of IowaWei Liu - IBM (United States)Jun Wang - Alibaba Group (China)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 21th ACM SIGKDD International Conference on knowledge discovery and data mining, Vol.2015-, pp.1405-1414
- Series
- KDD '15
- DOI
- 10.1145/2783258.2783389
- Publisher
- ACM
- Language
- English
- Date published
- 08/10/2015
- Academic Unit
- Computer Science
- Record Identifier
- 9984259415602771
Metrics
20 Record Views