In

Abstract

Progressing growth of IT has brought rapid technological advancement. Technologies are getting advance at an exponential rate and hence massive amount of data is emerging at very enormous rate in different sector. So there are lots of baselines for researcher to roadmap their strategy for technological improvement. Huge amount of data i.e. terabytes of data are carried over computer networks to and from organization working in the field of business, engineering and science. Many approaches based on mathematical model were suggested for dredging association rule but they were complex for users. Our work contemplated an algorithm for interpolating Stock Market data using fuzzy data dredging through which fuzzy association rule can be induced for Stock series. Our work proposes the algorithm in which each fuzzy item has its own predefined minimum support count. Time series data can be any sequence data which has some trend or pattern in it. It may be either stock market data, climatic observed data, data observed from medical equipments. Our work also measures the data dispersion in time series data i.e. stock market data used here. It shows the deviation of the stock prices from the mean of stock price data points taken over a period of time which help the investors to decide whether to buy or sell their shares or products. Risk associated with particular share can also be predicted by understanding the obtained curve in the experiment. We have implemented the contemplated work in WEKA tool to get more accurate and efficient result along with visualization. Basically we are predicting how data are interpreted and predicted with accuracy in stock market using this effective tool.