Application Of Density-Based Clustering Approaches For Stock Market Analysis

Present economy is largely dependent on the precise forecasting of the business avenues using the stock market data. As the stock market data falls under the category of big data, the task of handling becomes complex due to the presence of a large number of investment choices. In this paper, investigations have been carried out on the stock market data analysis using various density-based clustering approaches. For experimentation purpose, the stock market data from Quandl stock market was used. It was observed that the effectiveness of Dynamic Quantum clustering approach were better. This is because it has better adopting capability according of changing patterns of the stock market data. Similarly performances of other density-based clustering approaches like Weighted Adaptive Mean Shift Clustering, DBSCAN and Expectation Maximization and also partitive clustering methods such as k-means, k-medoids and fuzzy c means were also experimented on the same stock market data. The performance of all the approaches was tested in terms of standard measures. It was found that in majority of the cases, Dynamic Quantum clustering outperforms the other density-based clustering approaches. The algorithms were also subjected to paired t-tests which also confirmed the statistical significance of the results obtained.