Vector quantization and signal compression
Vector quantization and signal compression
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Fast k-nearest-neighbor search based on projection and triangular inequality
Pattern Recognition
Fast principal component analysis using fixed-point algorithm
Pattern Recognition Letters
A fast VQ codebook generation algorithm using codeword displacement
Pattern Recognition
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
Improvement of the k-means clustering filtering algorithm
Pattern Recognition
Finite-state vector quantization for waveform coding
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Fast-searching algorithm for vector quantization using projection and triangular inequality
IEEE Transactions on Image Processing
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
International Journal of Information Retrieval Research
Fast global k-means clustering based on local geometrical information
Information Sciences: an International Journal
A feature selection method using fixed-point algorithm for DNA microarray gene expression data
International Journal of Knowledge-based and Intelligent Engineering Systems
| Hi-index | 0.01 |
In this paper, we present a fast global k-means clustering algorithm by making use of the cluster membership and geometrical information of a data point. This algorithm is referred to as MFGKM. The algorithm uses a set of inequalities developed in this paper to determine a starting point for the jth cluster center of global k-means clustering. Adopting multiple cluster center selection (MCS) for MFGKM, we also develop another clustering algorithm called MFGKM+MCS. MCS determines more than one starting point for each step of cluster split; while the available fast and modified global k-means clustering algorithms select one starting point for each cluster split. Our proposed method MFGKM can obtain the least distortion; while MFGKM+MCS may give the least computing time. Compared to the modified global k-means clustering algorithm, our method MFGKM can reduce the computing time and number of distance calculations by a factor of 3.78-5.55 and 21.13-31.41, respectively, with the average distortion reduction of 5,487 for the Statlog data set. Compared to the fast global k-means clustering algorithm, our method MFGKM+MCS can reduce the computing time by a factor of 5.78-8.70 with the average reduction of distortion of 30,564 using the same data set. The performances of our proposed methods are more remarkable when a data set with higher dimension is divided into more clusters.