@article{1762,
  author = {Maya Nayak, Prasannajit Dash},
  title = {Distance-based and Density-based Outlier Detection on Time Series Data A Comparative Study with Stabilized Approach},
  journal = {Progress in Computing Applications},
  year = {2015},
  volume = {4},
  number = {1},
  doi = {},
  url = {},
  abstract = {Outlier detection always plays a major role in different industrial domains ranging from major financial fraud detection to network intrusions and also major clinical diagnosis of different diseases. Over the years of research, distance
based outlier detection algorithm and density based outlier detection algorithm have emerged as a viable, scalable, parameter free alternative way to the very long before the traditional approaches. In this paper, we evaluate both the distance and
density based outlier detection approach applying on a time-series data i.e. stock market data traded for three months. We begin by surveying and examining the design landscape of the above outlier detection approaches. Also implemented the outlier detection framework for the said design and analysis to understand the various pros and cons of various optimizations
proposed by us on a real-time data set i.e. the current stock market data set. The combinations of optimization techniques
(factors) and strategies through distance and density based outlier approaches always dominate on various types of data sets. So plays a dominant role on this real-time stock market data set.},
}