International Journal of Computer Vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
A General Approximation Technique for Constrained Forest Problems
SIAM Journal on Computing
Approximation algorithms for geometric problems
Approximation algorithms for NP-hard problems
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Introduction to Algorithms
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
On k-Median clustering in high dimensions
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
Algorithms for connected set cover problem and fault-tolerant connected set cover problem
Theoretical Computer Science
Linear-time approximation schemes for clustering problems in any dimensions
Journal of the ACM (JACM)
| Hi-index | 0.00 |
In this paper we study the problem of clustering entities that are described by two types of data: attribute data and relationship data. While attribute data describe the inherent characteristics of the entities, relationship data represent associations among them. Attribute data can be mapped to the Euclidean space, whereas that is not always possible for the relationship data. The relationship data is described by a graph over the vertices with edges denoting relationship between pairs of entities that they connect. We study clustering problems under the model where the relationship data is constrained by 'internal connectedness,' which requires that any two entities in a cluster are connected by an internal path, that is, a path via entities only from the same cluster. We study the k-median and k-means clustering problems under this model. We show that these problems are Ω(log n) hard to approximate and give O(log n) approximation algorithms for specific cases of these problems.