This paper considers a new clustering algorithm for processing time-evolving road anomaly reports. Two cluster categories, main and outlier, are defined to deal with outliers as well as to capture the evolving nature of road anomalies. The Mahalanobis distance is exploited to quantify the similarity between a new report and the existing clusters. The clusters are maintained online and the Woodbury matrix inverse lemma is used for their recursive updates. The proposed clustering algorithm can localize isolated anomalies and compress information for densely distributed anomalies. A simulation is presented to demonstrate the efficacy of the proposed algorithm.
A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance
2017
Article (Journal)
English
A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance
Online Contents | 2016
|Directional Mahalanobis Distance and Parameter Sensitivities
SAE Technical Papers | 2016
|Vector Quantization Based on Dynamic Adjustment of Mahalanobis Distance
British Library Conference Proceedings | 1996
|An adaptive fading Kalman filter based on Mahalanobis distance
SAGE Publications | 2015
|