A novel genetic algorithm for automatic clustering
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
An adaptive crossover-imaged clustering algorithm
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
An axis-shifted crossover-imaged clustering algorithm
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A deflected grid-based algorithm for clustering analysis
WSEAS Transactions on Computers
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Clustering, that is the partitioning of a set of patterns into disjoint and homogeneous meaningful groups (clusters), is a fundamental process in the practice of science. k -windows is an efficient clustering algorithm that reduces the number of patterns that need to be examined for similarity, using a windowing technique. It exploits well known spatial data structures, namely the range tree, that allows fast range searches. From a theoretical standpoint, the k -windows algorithm is characterized by lower time complexity compared to other well-known clustering algorithms. Moreover, it achieves high quality clustering results. However, it appears that it cannot be directly applicable in high-dimensional settings due to the superlinear space requirements for the range tree. In this paper, an improvement of the k -windows algorithm, aiming at resolving this defficiency, is presented. The improvement is based on an alternative solution to the orthogonal range search problem.