Open AccessA Hybrid Approach for Clustering Uncertain Time Series
Author(s) -
Ruizhe Ma,
Xiaoping Zhu,
Yan Li
Publication year - 2021
Publication title -
cit. journal of computing and information technology/journal of computing and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 27
eISSN - 1846-3908
pISSN - 1330-1136
DOI - 10.20532/cit.2020.1004802
Subject(s) - cluster analysis , computer science , data mining , cure data clustering algorithm , correlation clustering , series (stratigraphy) , data stream clustering , canopy clustering algorithm , set (abstract data type) , hierarchical clustering , uncertain data , artificial intelligence , paleontology , biology , programming language
Information uncertainty extensively exists in the real-world applications, and uncertain data process and analysis have been a crucial issue in the area of data and knowledge engineering. In this paper, we concentrate on uncertain time series data clustering, in which the uncertain values at time points are represented by probability density function. We propose a hybrid clustering approach for uncertain time series. Our clustering approach first partitions the uncertain time series data into a set of micro-clusters and then merges the micro-clusters following the idea of hierarchical clustering. We evaluate our approach with experiments. The experimental results show that, compared with the traditional UK-means clustering algorithm, the Adjusted Rand Index (ARI) of our clustering results have an obviously higher accuracy. In addition, the time efficiency of our clustering approach is significantly improved.