关键词: 密度聚类, 优化布谷鸟算法, 基于密度的聚类算法, MapReduce, 抗噪能力

Abstract:

In the process of parallel density clustering， the boundary points of clusters with different densities are divided fuzzy and there is data noise， which affects the clustering performance and makes the clustering results subject to the influence of local optimization. Therefore， a parallel density clustering algorithm MCS-KDBSCAN （maprule based parallel maximization cuckoo search K-means DBSCAN） based on MapReduce and optimized cuckoo algorithm is proposed. Firstly， the algorithm combines the strategy KDBSCAN（K-means DBSCAN）， which is based on the idea of nearest neighbor and inverse nearest neighbor in k-means. By calculating the influence space of each data point， the expansion conditions of clustering clusters in DBSCAN algorithm are redefined to avoid the problem of fuzzy boundary points of clustering clusters with different densities； Then， combined with the nearest neighbor idea in KDBSCAN density clustering， a feasible iterative noise point processing strategy is proposed to reduce the impact of noise points in data on the performance of clustering algorithm； Secondly， the optimization and improvement strategy MCS （maximization cuckoo search） based on the traditional cuckoo algorithm is proposed. By attenuating the weight of the probability of finding nests， with the increase of the number of iterative searches， the convergence speed of the algorithm is improved， and the influence of local optimization on the clustering results is solved； Finally， combined with MapReduce， a parallel density clustering strategy MCS-KDBSCAN is proposed. By parallelizing the operation of density clustering algorithm， the communication burden of local optimal solution transmission of parallel clustering algorithm is reduced and the performance of the algorithm is improved. Experiments show that the proposed mcs-kdbscan parallel density clustering algorithm is superior in clustering accuracy and clustering running time.

Key words: density clustering, optimization cuckoo algorithm, density-based spatial dutering of apptications with noise, MapReduce, resist noise ability