Clustering with Attribute-Level Constraints
2011
In many clustering applications the incorporation of background knowledge in the form of constraints is desirable. In this paper, we introduce a new constraint type and the corresponding clustering problem: attribute constrained clustering. The goal is to induce clusters of binary instances that satisfy constraints on the attribute level. These constraints specify whether instances may or may not be grouped to a cluster, depending on specific attribute values. We show how the well-established instance-level constraints, must-link and cannot-link, can be adapted to the attribute level. A variant of the k-Medoids algorithm taking into account attribute level constraints is evaluated on synthetic and real-world data. Experimental results show that such constraints may provide better clustering results at lower specification costs if constraints can be expressed on the attribute level.
Keywords:
- Machine learning
- Data mining
- Constraint satisfaction dual problem
- Constraint (mathematics)
- Correlation clustering
- Canopy clustering algorithm
- CURE data clustering algorithm
- Attribute domain
- Constrained clustering
- Artificial intelligence
- Mathematics
- Brown clustering
- Pattern recognition
- Computer science
- Fuzzy clustering
- Data stream clustering
- Cluster analysis
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
13
References
12
Citations
NaN
KQI