Information science plays a vital role in each and every field of science and technology, but it is facing several difficulties to handle the data and information, a main problem is data uncertainty, several theories are dealing with uncertainty, soft set theory also do vital role to handle this uncertainty problem. This paper analysed soft set reduction and how a sample dataset is converted into binary valued information system, and also analysed how binary valued information can be used to reduce dimension of data to take better decisions.

Forecasting based on time series data for stock prices, currency exchange rate, price indices, etc., is one of the active research areas in many field viz., finance, mathematics, physics, machine learning, etc. Initially, the problem of financial time sequences analysis and prediction are solved by many statistical models. During the past few decades, a large number of neural network models have been proposed to solve the problem of financial data and to obtain accurate prediction result. The statistical model integrated with ANN (Hybrid model) has given better result than using single model. This work discusses some basic ideas of time series data, need of ANN, importance of stock indices, survey of the previous works and it investigates neural network models for time series in forecasting. The forecasting accuracy is analyzed and measured with reference to an Indian stock market index such as Bombay Stock Exchange (BSE) and NIFTY MIDCAP50 in this study and it is found that the right parameters number of epochs, learning rate and momentum is 2960, 0.28 and 0.5 respectively for forecasting network by conducting various experiment.

In this paper, we present a novel approach to design a code book for vector quantization using standard deviation. The proposed algorithm optimizes the partitioning space to explore the search space for a set of equally viable and equivalent partitions. Essentially the partition space is partitioned into perceptive clusters, so that the code book is optimized. The proposed algorithm is proved better than the widely used quantization algorithm in applications.

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The clustering problem has been widely studied because it arises in many knowledge management oriented applications. It aims at identifying the distribution of patterns and intrinsic correlations in data sets by partitioning the data points into similarity clusters. Traditional clustering algorithms use distance functions to measure similarity centroid, which subside the influences of data points. Hence, in this article a novel non-distance based clustering algorithm is proposed which uses Combined Standard Deviation (CSD) as measure of similarity. The performance of CSD based K-means approach, called K-CSD clustering algorithm, is tested on synthetic data sets. It compared favorably to widely used K-means clustering algorithm.