Algorithms for clustering data
Algorithms for clustering data
Location- and Density-Based Hierarchical Clustering Using Similarity Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An efficient line symmetry-based K-means algorithm
Pattern Recognition Letters
Faster and more robust point symmetry-based K-means algorithm
Pattern Recognition
Minimax partial distortion competitive learning for optimal codebook design
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
We present a novel clustering algorithm (SDSA algorithm) based on the concept of the short distance of the consecutive points and the small angle between the consecutive vectors formed by three adjacent points. Not only the proposed SDSA algorithm is suitable for almost all test data sets used by Chung and Liu for point symmetry-based K-means algorithm (PSK algorihtm) and their newly proposed modified point symmetry-based K-means algorithm (MPSK algorithm ), the proposed SDSA algorithm is also suitable for many other cases where the PSK algorihtm and MPSK algorithm can not be well performed. Based on some test data sets, experimental results demonstrate that our proposed SDSA algorithm is rather encouraging when compared to the previous PSK algorithm and MPSK algorithm.