Computational geometry: an introduction
Computational geometry: an introduction
Algorithms for clustering data
Algorithms for clustering data
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Clustering with a minimum spanning tree of scale-free-like structure
Pattern Recognition Letters
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
An algorithm for point cluster generalization based on the Voronoi diagram
Computers & Geosciences
Comparative clustering analysis of bispectral index series of brain activity
Expert Systems with Applications: An International Journal
FPF-SB: a scalable algorithm for microarray gene expression data clustering
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index
IEEE Transactions on Knowledge and Data Engineering
Computers and Operations Research
Future Generation Computer Systems
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Clustering is an essential tool in data mining that has drawn enormous attention. In this paper, we present a new clustering algorithm with the help of Voronoi diagram. Here the clusters are formed by considering the neighboring Voronoi cells. The points belong to the closer Voronoi cells are merged to form the clusters. The similarity of the points is measured based on Euclidean distance of the neighboring points and hence it is not necessary to compare the distances from one point to all other points of the given set. We perform various experiments using many synthetic and biological data sets. The experimental results demonstrate the significance of the proposed method.