An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns

Abstract

Current time series clustering algorithms fail to effectively mine clustering distribution characteristics of time series data without sufficient prior knowledge. Furthermore, these algorithms fail to simultaneously consider the spatial attributes, non-spatial time series attribute values, and non-spatial time series attribute trends. This paper proposes an adaptive density-based time series clustering (DTSC) algorithm that simultaneously considers the three above-mentioned attributes to relieve these limitations. In this algorithm, the Delaunay triangulation is first utilized in combination with particle swarm optimization (PSO) to adaptively obtain objects with similar spatial attributes. An improved density-based clustering strategy is then adopted to detect clusters with similar non-spatial time series attribute values and time series attribute trends. The effectiveness and efficiency of the DTSC algorithm are validated by experiments on simulated datasets and real applications. The results indicate that the proposed DTSC algorithm effectively detects time series clusters with arbitrary shapes and similar attributes and densities while considering noises.

Traffic Status Evolution Trend Prediction Based on Congestion Propagation Effects under Rainy Weather

“…Cluster analysis maximizes the similarity of objects in a single class and minimizes the similarity of objects between different classes. In traffic data mining, commonly used clustering algorithms include partition-based k-means algorithm [10,11], hierarchical clustering algorithm [12], density-based spatial clustering of applications with noise algorithm [13,14], and FCM clustering algorithm [15][16][17].…”

“…e density-based spatial clustering of applications with noise algorithm also has a fast clustering speed. is algorithm can effectively process noisy data to find spatial clusters of arbitrary shapes and does not require entering the number of clusters during the clustering process [13]. However, when the density of spatial clustering is not uniform and the cluster spacing is large, the clustering results obtained by the density-based spatial clustering of applications with noise algorithm are poor.…”