Computer Science ›› 2018, Vol. 45 ›› Issue (2): 287-290.doi: 10.11896/j.issn.1002-137X.2018.02.049

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Study on Fast Incremental Clustering Algorithm for High Complexity Dynamic Data in Cloud Computing Environment

CHEN Gan-lang, YAN Fei-long and PAN Jia-hui   

  • Online:2018-02-15 Published:2018-11-13

Abstract: In order to solve the problems that the traditional clustering algorithm has the disadvantages of high cost,poor clustering quality and slow clustering speed,this paper proposed a new fast clustering algorithm based on incremental density of high complexity dynamic data in cloud computing environment.First of all,on the basis of density under the environment of high complexity of dynamic data clustering in cloud computing,this algorithm finds some subspace from the data space.The data mapped to the space area can produce high density point set,and the set of connec-ted regions is regarded as the clustering results.Secondly,it executes incremental clustering by DBSCAN algorithm, and studies the original clustering merger or split caused by inserting or deleting data.Finally,by dealing with all the core data in the neighborhood of changing the core status in the process of updating,the incremental clustering is analyzed from two aspects of inserting or deleting data.The experimental results show that the proposed algorithm has the cha-racteristics of low cost,fast clustering speed and high clustering quality.

Key words: Cloud computing environment,High complexity,Dynamic data,Incremental density,Fast clustering

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