Algorithms for clustering data
Algorithms for clustering data
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Density biased sampling: an improved method for data mining and clustering
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Bootstrap Technique for Nearest Neighbor Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability-based validation of clustering solutions
Neural Computation
Practical solutions to the problem of diagonal dominance in kernel document clustering
ICML '06 Proceedings of the 23rd international conference on Machine learning
EURASIP Journal on Applied Signal Processing
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Recently, stability-based techniques have emerged as a very promising solution to the problem of cluster validation. An inherent drawback of these approaches is the computational cost of generating and assessing multiple clusterings of the data. In this paper we present an efficient prediction-based validation approach suitable for application to large, high-dimensional datasets such as text corpora. We use kernel clustering to isolate the validation procedure from the original data. Furthermore, we employ a prototype reduction strategy that allows us to work on a reduced kernel matrix, leading to significant computational savings. To ensure that this condensed representation accurately reflects the cluster structures in the data, we propose a density-biased strategy to select the reduced prototypes. This novel validation process is evaluated on real-world text datasets, where it is shown to consistently produce good estimates for the optimal number of clusters.