A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
New methods for the initialisation of clusters
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A comparison of several vector quantization codebook generation approaches
IEEE Transactions on Image Processing
An efficient k'-means clustering algorithm
Pattern Recognition Letters
Tracking Data Structures Coherency in Animated Ray Tracing: Kalman and Wiener Filters Approach
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Clustering Multivariate Normal Distributions
Emerging Trends in Visual Computing
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Fuzzy Systems
Fast kd-tree construction for 3D-rendering algorithms like ray tracing
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
K-means clustering seeds initialization based on centrality, sparsity, and isotropy
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
A robust iterative refinement clustering algorithm with smoothing search space
Knowledge-Based Systems
Signal identification of block orthogonal modulations
RWS'10 Proceedings of the 2010 IEEE conference on Radio and wireless symposium
Improving the performance of k-means for color quantization
Image and Vision Computing
Application of K-Medoids with Kd-Tree for Software Fault Prediction
ACM SIGSOFT Software Engineering Notes
An improved rough clustering using discernibility based initial seed computation
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Expert Systems with Applications: An International Journal
MicroCBR: A case-based reasoning architecture for the classification of microarray data
Applied Soft Computing
Graph based k-means clustering
Signal Processing
A BIRCH-Based clustering method for large time series databases
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Clustering biological data using voronoi diagram
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
International Journal of Information Retrieval Research
CRUDAW: a novel fuzzy technique for clustering records following user defined attribute weights
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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We present a method for initialising the K-means clustering algorithm. Our method hinges on the use of a kd-tree to perform a density estimation of the data at various locations. We then use a modification of Katsavounidis' algorithm, which incorporates this density information, to choose K seeds for the K-means algorithm. We test our algorithm on 36 synthetic datasets, and 2 datasets from the UCI Machine Learning Repository, and compare with 15 runs of Forgy's random initialisation method, Katsavounidis' algorithm, and Bradley and Fayyad's method.