Algorithms for clustering data
Algorithms for clustering data
K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel programming with MPI
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Digital Image Processing
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Parallel Hierarchical Clustering Algorithms
IEEE Transactions on Parallel and Distributed Systems
Effectively Utilizing Global Cluster Memory for Large Data-Intensive Parallel Programs
IEEE Transactions on Parallel and Distributed Systems
Parallel bisecting k-means with prediction clustering algorithm
The Journal of Supercomputing
Bounded-Collision Memory-Mapping Schemes for Data Structures with Applications to Parallel Memories
IEEE Transactions on Parallel and Distributed Systems
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters
IEEE Transactions on Knowledge and Data Engineering
Parallel Point Symmetry Based Clustering for Gene Microarray Data
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Space and time efficient parallel algorithms and software for EST clustering
IEEE Transactions on Parallel and Distributed Systems
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An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently developed point symmetry-based distance norm is proposed. The proposed distributed computing time efficient point symmetry based K-Means technique is able to correctly identify presence of overlapping clusters of any arbitrary shape and size, whether they are intra-symmetrical or inter-symmetrical in nature. A Kd-tree based approximate nearest neighbor searching technique is used as a speedup strategy for computing the point symmetry based distance. Superiority of this new parallel implementation with the novel two-phase speedup strategy over existing parallel K-Means clustering algorithm, is demonstrated both quantitatively and in computing time, on two SPOT and Indian Remote Sensing satellite images, as even K-Means algorithm fails to detect the symmetry in clusters. Different land cover regions, classified by the algorithms for both images, are also compared with the available ground truth information. The statistical analysis is also performed to establish its significance to classify both satellite images and numeric remote sensing data sets, described in terms of feature vectors.