چكيده به لاتين
Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. here, we propose an efficient clustering algorithm by MapReduce paradigm. we adopt a quick partitioning strategy for large scale non-indexed data. The traditional K-means clustering algorithm is difficult to initialize the number of clusters K, and the initial cluster centers are selected randomly, this makes the clustering results very unstable. Meanwhile, algorithms are susceptible to noise points. To solve the problems, the traditional K-means algorithm is improved. The improved method is divided into the same grid in space, according to the size of the data point property value and assigns it to the corresponding grid. And count the number of data points in each grid. We will parallel the improved k-mean algorithm and combined with the MapReduce framework. Theoretical analysis and experimental results show that the improved algorithm compared to the traditional K-means clustering algorithm has high quality results, less iteration and has good stability. Results for algorithms of here reveal that the speedup and scaleupof our work are very efficient.
Keywords: DBSCAN; MapReduce; parallel system; Cluster analysis, K-means, Grid
