A Model-Based Multivariate Time Series Clustering Algorithm

Zhou, Pei-Yuan; Chan, Kwong

doi:10.1007/978-3-319-13186-3_72

Cited by 15 publications

(15 citation statements)

References 13 publications

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“…K-means (Algorithm 1) [6], used in this work, belongs to partitioning clustering methods and is popular due to its simplicity. It is based on the squared error minimization method and the main advantage of K-means is that, in each iteration, only the distance between a point and the K cluster centers is computed.…”

Section: Clusteringmentioning

confidence: 99%

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

et al. 2018

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In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data. The k-anonymity set consists of the k nearest neighbors. We constructed a motion vector of the form (x,y,g,v) where x and y are the spatial coordinates, g is the angle direction, and v is the velocity of mobile users, and studied the problem in four-dimensional space. We followed two approaches. The former applied only k-Nearest Neighbor (k-NN) algorithm on the whole dataset, while the latter combined trajectory clustering, based on K-means, with k-NN. Actually, it applied k-NN inside a cluster of mobile users with similar motion pattern (g,v). We defined a metric, called vulnerability, that measures the rate at which k-NNs are varying. This metric varies from 1 k (high robustness) to 1 (low robustness) and represents the probability the real identity of a mobile user being discovered from a potential attacker. The aim of this work was to prove that, with high probability, the above rate tends to a number very close to 1 k in clustering method, which means that the k-anonymity is highly preserved. Through experiments on real spatial datasets, we evaluated the anonymity robustness, the so-called vulnerability, of the proposed method.

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Section: Clusteringmentioning

confidence: 99%

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

et al. 2018

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“…This clustering approach was used for fault detection in a gas turbine. Zhou and Chan (2014) developed an algorithm for clustering MVTS by discovering each TS's temporal patterns. Their algorithm is based on k-means and aims to groups MVTS with similar temporal patterns together into the same cluster.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Multi-variate Time Series Clustering for Imputation of Air Pollution Data

Alahamade

Lake

Iglesia

2021

Preprint

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Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a Multivariate Time Series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutants concentrations produced very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours, and of the characteristics of different stations according to their environmental type.

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“…This method focuses on clustering different MTS datasets and requires well-established a priori information of initial classes. Zhou and Chan [12] proposed a model-based clustering algorithm to cluster MTS based on the discovered temporal patterns in each MTS and compare them with those discovered in the others so that MTS that exhibit similar patterns can be grouped together in the same cluster. This method discovers temporal patterns using confidence value (a.k.a lift ratio) to represent the relationship between different variables.…”

Section: Multivariate Time-series Analysismentioning

confidence: 99%

“…In this section, we present an approach to mine MSTS. Given a set of MSTS, the proposed approach incorporates an effective initial MTS pattern mining algorithm [12] to detect temporal patterns in a set of MTS for each location. Then, we propose a new algorithm to detect co-occurrence of the discovered temporal patterns across locations by mining a transformed spatio-temporal pattern matrix (STPM) that characterizes the space to form spatio-temporal patterns.…”

Section: The Multivariate Spatial Time-series Pattern Discovery Problemmentioning

confidence: 99%

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Discovery of Spatio-Temporal Patterns in Multivariate Spatial Time Series

Chan

2020

ACM/IMS Trans. Data Sci.

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With the advancement of the computing technology and its wide range of applications, collecting large sets of multivariate time series in multiple geographical locations introduces a problem of identifying interesting spatio-temporal patterns. We consider a new spatial structure of the data in the pattern discovery process due to the dependent nature of the data. This article presents an information-theoretic approach to detect the temporal patterns from the multivariate time series in multiple locations. Based on their occurrences of discovered temporal patterns, we propose a method to identify interesting spatio-temporal patterns by a statistical significance test. Furthermore, the identified spatio-temporal patterns can be used for clustering and classification. For evaluating the performance, a simulated dataset is tested to validate the quality of the identified patterns and compare with other approaches. The result indicates the approach can effectively identify useful patterns to characterize the dataset for further analysis in achieving good clustering quality. Furthermore, experiments on real-world datasets and case studies have been conducted to illustrate the applicability and the practicability of the proposed approach. CCS Concepts: • Mathematics of computing → Time series analysis; Cluster analysis; • Information systems → Data mining;

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A Model-Based Multivariate Time Series Clustering Algorithm

Cited by 15 publications

References 13 publications

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

Evaluation of Multi-variate Time Series Clustering for Imputation of Air Pollution Data

Discovery of Spatio-Temporal Patterns in Multivariate Spatial Time Series

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