Analysing the Stock Market Using Clustering in Time Series Data Mining

A new framework for analysing time series data called Time Series Data Mining (TSDM) is introduced. This framework adapts and innovate data mining concepts to analysing time series data. It creates a set of methods with the growing deployment of a large Number of sensors, telemetry devices and that reveals hidden temporal patterns that are characteristic and predictive of time series events. The TSDM methods overcome limitations of traditional time series analysis techniques by adapting data mining concepts for analysing time series. Data Mining is the analysis of data with the goal of uncovering hidden patterns. Data Mining encompasses a set of methods that automate the scientific discovery process. In general, applied the clustering algorithm to stock market data and showed the results based on the stock market index (high return, low return, high risk and low risk). Therefore in this paper applied K-Means Clustering algorithm is applied for Stock Market. The goal of stock market data clustering is to predict the period of highly returned investments. The clustering algorithms were successfully applied to cluster stock market data comprising into two distinct clusters based on the similarity of stock market index profiles and prior share market knowledge.