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
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On coresets for k-means and k-median clustering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
A Simple Linear Time (1+ ") -Approximation Algorithm for k-Means Clustering in Any Dimensions
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
The anchors hierarchy: using the triangle inequality to survive high dimensional data
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Bandwidth adaptive hardware architecture of K-Means clustering for video analysis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A voronoi diagram approach to autonomous clustering
DS'06 Proceedings of the 9th international conference on Discovery Science
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Clustering is a basic tool in unsupervised machine learning and data mining. One of the simplest clustering approaches is the iterative k-means algorithm. The quality of k-means clustering suffers from being confined to run with fixed k rather than being able to dynamically alter the value of k. Moreover, it would be much more elegant if the user did not have to supply the number of clusters for the algorithm. In this paper we consider recently proposed autonomous versions of the k-means algorithm. We demonstrate some of their shortcomings and put forward solutions for their deficiencies. In particular, we examine the problem of automatically determining a good initial candidate as the number of clusters.