Clustering multivariate time‐series data

Singhal, Anupam; Seborg, Dale E.

doi:10.1002/cem.945

Cited by 137 publications

(54 citation statements)

References 39 publications

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“…PCA variable loadings allow a significant reduction in the number of response surfaces to be analyzed to obtain a superior understanding of the optimization procedure [12]. Clustering methodology is based on calculating the degree of similarity using PCA and distance-similarity factors [13]. This methodology is a promising tool to efficiently interpret and analyze experimental data [14].…”

Section: Introductionmentioning

confidence: 99%

Optimization and characterization of walnut beverage emulsions in relation to their composition and structure

Gharibzahedi

Mousavi

Khodaiyan

et al. 2012

International Journal of Biological Macromolecules

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Section: Introductionmentioning

confidence: 99%

Optimization and characterization of walnut beverage emulsions in relation to their composition and structure

Gharibzahedi

Mousavi

Khodaiyan

et al. 2012

International Journal of Biological Macromolecules

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“…It is well known that cluster analysis is about finding groups in datasets (Singhal and Seborg, 2005). Data clustering is an important method to analyze a data set according to find its structure.…”

Section: Methodsmentioning

confidence: 99%

Clustering of OECD Countries Out of Pocket Health Expenditure Time Series Data

Çınaroğlu

2016

RAE

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Out of pocket health expenditures points out to the payments made by households at the point they receive health services. Frequently these include doctor consultation fees, purchase of medication and hospital bills. In this study hierarchical clustering method was used for classification of 34 countries which are members of OECD (Organization for Economic Cooperation and Development) in terms of out of pocket health expenditures for the years between 1995-2011. Longest common subsequences (LCS), correlation coefficient and Euclidean distance measure was used as a measure of similarity and distance in hierarchical clustering. At the end of the analysis it was found that LCS and Euclidean distance measures were the best for determining clusters. Furthermore, study results led to understand grouping of OECD countries according to health expenditures.

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“…PCA, as a feature extraction method, is effectively applied to time series data [20], [28], [34], [35]. It is often utilized to reduce the dimensions of a d-dimensional dataset by projecting it onto a w-dimensional subspace where w is less than d.…”

Section: Related Workmentioning

confidence: 99%

Towards Visual Exploration of Large Temporal Datasets

Ali

Jones

Xie

et al. 2018

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)

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We address the problem of visualizing and interacting with large multi-dimensional time-series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and enable exploration of large temporal datasets. Our approach consists of three main stages which are preprocessing, dimensionality reduction, and visual exploration. It assists with finding the interesting features in the data which are often obscured in the line chart because of the visual compression that is required to render the large dataset to screen. Our approach helps to obtain an overview of the entire dataset and track changes over time. It enables the user to detect clusters and outliers and observe the transitions between data. The juxtaposed views are used to visualize and interact both with raw time series data and projected data. Different time series datasets are deployed on our system, and we demonstrate the utility and evaluate the results using a case study with two different datasets which show the effectiveness of our system.

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Clustering multivariate time‐series data

Cited by 137 publications

References 39 publications

Optimization and characterization of walnut beverage emulsions in relation to their composition and structure

Optimization and characterization of walnut beverage emulsions in relation to their composition and structure

Clustering of OECD Countries Out of Pocket Health Expenditure Time Series Data

Towards Visual Exploration of Large Temporal Datasets

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