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ParSymG: a parallel clustering approach for unsupervised classification of remotely sensed imagery

International Journal of Digital Earth 2017年第5期10卷 471-489页

作者： Zhenhong Du Yuhua Gu Chuanrong Zhang Feng Zhang Renyi Liu Jean Sequeira Weidong Li Geographic Information Science Institution School of Earth SciencesZhejiang UniversityHangzhouPeople’s Republic of China Zhejiang Key Laboratory of Geographic Information System Zhejiang UniversityHan gzhouPeople’s Republic of China Geography and Center for Envrionmental Sciences and Engineering University of ConnecticutStorrsCTUSA Department of Computer Science Aix-Marseille UniversityMarseilleFrance

Symmetry is a common feature in the real *** may be used to improve a classification by using the point symmetry-based distance as a measure of ***,it is time consuming to calculate the point symmetry-based *** an efficient parallel point symmetry-based K-means algorithm(ParSym)has been propsed to overcome this limitation,ParSym may get stuck in sub-optimal solutions due to the K-means technique it *** this study,we proposed a novel parallel point symmetry-based genetic clustering(ParSymG)algorithm for unsupervised *** genetic algorithm was introduced to overcome the sub-optimization problem caused by inappropriate selection of initial centroids in ParSym.A message passing interface(MPI)was used to implement the distributed master–slave *** make the algorithm more time-efficient,a three-phase speedup strategy was adopted for population initialization,image partition,and kd-tree structure-based nearest neighbor *** advantages of ParSymG over existing ParSym and parallel K-means(PKM)alogithms were demonstrated through case studies using three different types of remotely sensed *** in speedup and time gain proved the excellent scalability of the ParSymG algorithm.

关键词： Unsupervised classification parallel clustering genetic algorithm point symmetry-based distance

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A Survey and Experimental Review on Data Distribution Strategies for parallel Spatial clustering Algorithms

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Journal of Computer Science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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