ABSTRACT
The need to understand large, complex, information-rich data sets is common to all fields of studies in this current information age. Given this tremendous amount of data, efficient and effective tools need to be present to analyze and reveal valuable knowledge that is hidden within the data. Clustering analysis is one of the popular approaches in data mining and has been widely used in big data analysis. The goal of clustering involves the task of dividing data points into homogeneous groups such that the data points in the same group are as similar as possible and data points in different groups are as dissimilar as possible. The importance of clustering is documented in pattern recognition, machine learning, image analysis, information retrieval, etc.
Due to the difficulties of parallelization of the clustering algorithms and the inefficiency at large scales, challenges for applying clustering techniques in big data have arisen. The question is how to deploy clustering algorithms to this tremendous amount of data to get the clustering result within a reasonable time. This chapter provides an overview of the mainstream clustering techniques proposed over the past decade and the trend and progress of clustering algorithms applied in big data. Moreover, the improvement of clustering algorithms in big data is introduced and analyzed. 334The possible future for more advanced clustering techniques is illuminated on the basis of today's information era.
