PROJECT TITLE :

PPHOPCM: Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering with Cloud Computing - 2017

ABSTRACT:

Collectively important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means that algorithm (PCM) has been widely used in image analysis and data discovery. However, it's tough for PCM to produce a good result for clustering big knowledge, particularly for heterogenous information, since it is initially designed for solely tiny structured dataset. To tackle this downside, the paper proposes a high-order PCM algorithm (HOPCM) for large data clustering by optimizing the objective perform within the tensor area. Any, we tend to design a distributed HOPCM methodology based on MapReduce for very massive amounts of heterogeneous data. Finally, we have a tendency to devise a privacy-preserving HOPCM algorithm (PPHOPCM) to guard the non-public knowledge on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV theme. Experimental results indicate that PPHOPCM will effectively cluster a large variety of heterogeneous data using cloud computing without disclosure of non-public knowledge.