Algorithms for clustering data
Algorithms for clustering data
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
Modified global k-means algorithm for clustering in gene expression data sets
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Fast global k-means clustering using cluster membership and inequality
Pattern Recognition
Ant clustering algorithm with K-harmonic means clustering
Expert Systems with Applications: An International Journal
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
Expert Systems with Applications: An International Journal
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat non-hierarchical clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters k and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points.