A weighted kernel possibilistic c-means algorithm based on cloud computing for clustering big data

Possibilistic c-means (PCM) cluster algorithm has emerged as an important tool for data preprocessing widely used in data mining and knowledge discovery. Owning to the huge amount of data, high computational complexity, and noise-corrupted data, the PCM algorithms scaled for big data find it difficult to produce a good result in real time. The paper proposes a weighted kernel PCM (wkPCM) algorithm to cluster data objects in appropriate groups. The proposed algorithm introduces weights to define the relative importance of each object in the kernel clustering solution, which reduces the corruption caused by noisy data. In order to improve the real time of the proposed algorithm, cloud computing technology is used to optimize wkPCM to propose a distributed wkPCM algorithm based on MapReduce, which can provide significant computation speed. Experiment demonstrates that the proposed possibilistic clustering algorithms can cluster big data in appropriate groups in real time. Copyright (c) 2014 John Wiley & Sons, Ltd.