Kinetics of Oxidation of 2-Mercaptoethylamine Hydrochloride by 2,6-Dichlorophenolindophenol in Methanol-Water Medium

1079-108

Privacy Preserving Clustering In Data Mining

Huge volume of detailed personal data is regularly collected and sharing of these data is proved to be beneficial for data mining application. Such data include shopping habits, criminal records, medical history, credit records etc .On one hand such data is an important asset to business organization and governments for decision making by analyzing it .On the other hand privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accurate clustering result. Trying to give solution for this we implemented vector quantization approach piecewise on the datasets which segmentize each row of datasets and quantization approach is performed on each segment using K means which later are again united to form a transformed data set. Some experimental results are presented which tries to finds the optimum value of segment size and quantization parameter which gives optimum in the tradeoff between clustering utility and data privacy in the input dataset

Spectra of Anderson type models with decaying randomness

In this paper we consider some Anderson type models, with free parts having long range tails and with the random perturbations decaying at different rates in different directions and prove that there is a.c. spectrum in the model which is pure. In addition, we show that there is pure point spectrum outside some interval. Our models include potentials decaying in all directions in which case absence of singular continuous spectrum is also shown