An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

Author(s)	Mr. P Isaac Paul, Mr. A. V Ramana, Mr. N Vara Prasad
Country	India
Abstract	In this paper, two machine learning algorithms—local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN)—that are used to identify outliers in the context of a continuous framework for point of interest (PoI) detection are analyzed. The mobile trajectories of users are continuously and almost instantaneously loaded into this system. These frameworks are still in their infancy, but they are already essential for large-scale sensing deployments, such as Smart City planning deployments, where the anonymous individual mobile user trajectories can be valuable to improve urban planning. There are two contributions made by this paper. First, the functional design of the entire PoI detection architecture is provided by the study. Second, the study evaluates the effectiveness.
Keywords	outliers; DBSCAN; LOF; GPS trajectories; machine learning.
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-07
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.16643

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