Algorithms for clustering data
Algorithms for clustering data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Concept decompositions for large sparse text data using clustering
Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
CLUMP: A Scalable and Robust Framework for Structure Discovery
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
CLUMP: A Scalable and Robust Framework for Structure Discovery
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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We introduce a robust and efficient framework called CLUMP (CLustering Using Multiple Prototypes) for unsupervised discovery of structure in data. CLUMP relies on finding multiple prototypes that summarize the data. Clustering the prototypes enables our algorithm to scale up to extremely large and high-dimensional domains such as text data. Other desirable properties include robustness to noise and parameter choices. In this paper, we describe the approach in detail, characterize its performance on a variety of datasets, and compare it to some existing model selection approaches.