Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions

In recent times, dimension size has posed more challenges as compared to data size. The serious concern of high dimensional data is the curse of dimensionality and has ultimately caught the attention of data miners. Anomaly detection based on local neighborhood like local outlier factor has been admitted as state of art approach but fails when operated on the high number of dimensions for the reason mentioned above. In this paper, we determine the effects of different distance functions on an unlabeled dataset while digging outliers through the density-based approach. Further, we also explore findings regarding runtime and outlier score when dimension size and number of nearest neighbor points (min_pts) are varied. This analytic research is also very appropriate and applicable in the domain of big data and data science as well.

@article{Rehman2020,
  title     = {Local Neighborhood-based Outlier Detection of High Dimensional Data using different Proximity Functions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {4},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Mujeeb Ur Rehman and Dost Muhammad Khan},
  doi       = {10.14569/IJACSA.2020.0110418},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110418}
}