Computational geometry: an introduction
Computational geometry: an introduction
Filtering search: a new approach to query answering
SIAM Journal on Computing
Algorithms for clustering data
Algorithms for clustering data
A conceptual version of the K-means algorithm
Pattern Recognition Letters
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Halo World: Tools for Parallel Cluster Finding inAstrophysical N-body Simulations
Data Mining and Knowledge Discovery
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
On distributing the clustering process
Pattern Recognition Letters
A Divise Initialisation Method for Clustering Algorithms
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Large-Scale Parallel Data Clustering
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
On distributing the clustering process
Pattern Recognition Letters
Unsupervised clustering on dynamic databases
Pattern Recognition Letters
A method for personalized clustering in data intensive web applications
Proceedings of the joint international workshop on Adaptivity, personalization & the semantic web
Cell-nuclear data reduction and prognostic model selection in bladder tumor recurrence
Artificial Intelligence in Medicine
Mining customer knowledge for product line and brand extension in retailing
Expert Systems with Applications: An International Journal
Mining stock category association and cluster on Taiwan stock market
Expert Systems with Applications: An International Journal
On clustering tree structured data with categorical nature
Pattern Recognition
Mining demand chain knowledge of life insurance market for new product development
Expert Systems with Applications: An International Journal
Automatic Image Annotation Using Color K-Means Clustering
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Expert Systems with Applications: An International Journal
A new-fangled FES-k-Means clustering algorithm for disease discovery and visual analytics
EURASIP Journal on Bioinformatics and Systems Biology
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Privacy preserving unsupervised clustering over vertically partitioned data
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
Unit volume based distributed clustering using probabilistic mixture model
DS'05 Proceedings of the 8th international conference on Discovery Science
Information Sciences: an International Journal
Mining shopping behavior in the Taiwan luxury products market
Expert Systems with Applications: An International Journal
A new hierarchical clustering algorithm
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Generalizing the k-Windows clustering algorithm in metric spaces
Mathematical and Computer Modelling: An International Journal
Expert Systems with Applications: An International Journal
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The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called Clustering in the literature and it is applied in various fields including data mining, statistical data analysis, compression and vector quantization. The k-means is a very popular algorithm and one of the best for implementing the clustering process. The k-means has a time complexity that is dominated by the product of the number of patterns, the number of clusters, and the number of iterations. Also, it often converges to a local minimum. In this paper, we present an improvement of the k-means clustering algorithm, aiming at a better time complexity and partitioning accuracy. Our approach reduces the number of patterns that need to be examined for similarity, in each iteration, using a windowing technique. The latter is based on well known spatial data structures, namely the range tree, that allows fast range searches.