A linear-time algorithm for computing the Voronoi diagram of a convex polygon
Discrete & Computational Geometry
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
CG '91 Proceedings of the International Workshop on Computational Geometry - Methods, Algorithms and Applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
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
On autonomous k-means clustering
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
A multi-prototype clustering algorithm
Pattern Recognition
NNCluster: an efficient clustering algorithm for road network trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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Clustering is a basic tool in unsupervised machine learning and data mining. Distance-based clustering algorithms rarely have the means to autonomously come up with the correct number of clusters from the data. A recent approach to identifying the natural clusters is to compare the point densities in different parts of the sample space. In this paper we put forward an agglomerative clustering algorithm which accesses density information by constructing a Voronoi diagram for the input sample. The volumes of the point cells directly reflect the point density in the respective parts of the instance space. Scanning through the input points and their Voronoi cells once, we combine the densest parts of the instance space into clusters. Our empirical experiments demonstrate the proposed algorithm is able to come up with a high-accuracy clustering for many different types of data. The Voronoi approach clearly outperforms k-means algorithm on data conforming to its underlying assumptions.