Algorithms for clustering data
Algorithms for clustering data
Elements of information theory
Elements of information theory
Concept Decompositions for Large Sparse Text Data Using Clustering
Machine Learning
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Generative model-based clustering of directional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Neural Networks
Designing semantics-preserving cluster representatives for scientific input conditions
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Bregman bubble clustering: A robust framework for mining dense clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Explicit learning curves for transduction and application to clustering and compression algorithms
Journal of Artificial Intelligence Research
Non-parametric mixture models for clustering
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Cluster validity measures based on the minimum description length principle
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Hi-index | 0.00 |
We propose and test an objective criterion for evaluation of clustering performance: How well does a clustering algorithm run on unlabeled data aid a classification algorithm? The accuracy is quantified using the PAC-MDL bound [3] in a semisupervised setting. Clustering algorithms which naturally separate the data according to (hidden) labels with a small number of clusters perform well. A simple extension of the argument leads to an objective model selection method. Experimental results on text analysis datasets demonstrate that this approach empirically results in very competitive bounds on test set performance on natural datasets.