Clustering analysis method and implementation in the MapReduce model for solving data-intensive problems

Abstract

ABSTRACT In data-intensive environment, data mining and data analysis is a big challenge. It analyses basic algorithms, such as k-means algorithm and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). And both algorithms are implemented on MapReduce of Hadoop. Further, an improved Data-intensive Density Base Distributed Clustering (IDBDC) algorithm is proposed which are based on k-means algorithm and DBSCAN algorithm. And IDBDC algorithm is implemented on MapReduce of Hadoop as well. The experimental results show that the improved IDBDC algorithm has better performance.