Paper Info
Title
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.
Year
DOI
Venue
2016
10.3390/ijgi5110205
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
Field
DocType
time series clustering,adaptive,density-based clustering,Delaunay triangulation,spatial data mining
Time series,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Artificial intelligence,Cluster analysis,Mathematics,Delaunay triangulation
Journal
Volume
Issue
ISSN
5
11
2220-9964
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Xiaomi Wang121.11
Yaolin Liu29725.42
Yiyun Chen3117.68
Yi Liu483.06