PRIVACY PRESERVING DATA MINING TECHNIQUES USING RECENT ALGORITHMS

The privacy preserving data mining is playing crucial role act as rising technology to perform various data mining operations on private data and to pass on data in a secured way to protect sensitive data. Many types of technique such as randomization, secured sum algorithms and k-anonymity have been suggested in order to execute privacy preserving data mining. In this survey paper, on current researches made on privacy preserving data mining technique with fuzzy logic, neural network learning, secured sum and various encryption algorithm is presented. This will enable to grasp the various challenges faced in privacy preserving data mining and also help us to find best suitable technique for various data environment

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory