云计算环境下高复杂度动态数据的增量密度快速聚类算法研究

针对传统的聚类算法存在开销大、聚类质量差、聚类速度慢等问题,提出一种新的云计算环境下高复杂度动态数据的增量密度快速聚类算法。首先,依据密度对云计算环境下高复杂度动态数据进行聚类,从数据空间中找到部分子空间,使得数据映射至该空间后可产生高密度点集区域,将连通区域的集合看作聚类结果；其次,通过DBSCAN算法进行增量聚类,并对插入或删除数据导致的原聚类合并或分裂进行研究；最后,在更新的过程中通过改变核心状态数据的邻域中含有的全部核心数据进行处理,从插入或删除数据两方面进行增量聚类分析。实验结果表明,所提算法开销低、聚类速度快、聚类质量高。

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

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.