An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

Abstract

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture the trend and evolution of moving object clusters pattern. Firstly, the density peaks clustering algorithm is exploited to identify clusters of different snapshots. The stable relationship between relatively few moving objects is used to improve the clustering efficiency. Then, by intersecting clusters from different snapshots, the gradual moving object clusters pattern is updated. The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process. Finally, experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System

“…In [ 33 ], a fully online clustering algorithm is proposed for clustering evolving data streams into arbitrarily shaped clusters (CEDAS), which is also a density-based clustering algorithm. In [ 34 ], a density-based clustering algorithm called DStream-GC is designed for discovering gradual moving object clusters pattern from trajectory streams. In [ 35 ], a self-organizing incremental neural network (SOINN+) is developed for unsupervised learning clusters with arbitrary shapes from noisy data.…”