K-Means Clustering with Bagging and MapReduce

Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first intuition about their data by identifying meaningful groups among the data objects. K-means is one of the most famous clustering algorithms. Its simplicity and speed allow it to run on large data sets. However, it also has several drawbacks. First, this algorithm is instable and sensitive to outliers. Second, its performance will be inefficient when dealing with large data sets. In this paper, a method is proposed to solve those problems, which uses an ensemble learning method bagging to overcome the instability and sensitivity to outliers, while using a distributed computing framework MapReduce to solve the inefficiency problem in clustering on large data sets. Extensive experiments have been performed to show that our approach is efficient.