Temporal gap statistic: A new internal index to validate time series clustering

Ribeiro, Rosana Guimarães; Rios, Ricardo Araújo

doi:10.1016/j.chaos.2020.110326

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Evaluating the quality of clustering methods is as important as the clustering algorithms themselves. Here are some clustering validity indices (CVI) [1,2,3,4,5] that evaluate the quality of the solution, and these CVIs have commonly been used in many fields [6,7,3]. Literature [8,9] proposed validity index based on density, which was computed by using Euclidean Distance.…”

Section: Introductionmentioning

confidence: 99%

A New Cluster Validity Index for Fuzzy Clustering Using Separation and Compactness

Chen¹,

Zhong²,

Chen

et al. 2023

Preprint

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Aiming at weak point of classical validity index based on Euclidean distance, we proposed a new validity index which based on Non-Euclidean Distance. First of all, we constructed compact index by using improved membership degree and then made use of anti-close degree to constructed separation index. Finally, a new validity index based on these was proposed. This method overcame the drawbacks of classical validity index based on Euclidean distance, which did not correctly reflect the relation of intra-class and inter-class. Experiments on different datasets show that new method has good performance in noise data environment. The capacity of new method is superior to others with regard to cope with overlap dataset. New method has more obvious superiority especially on high dimensional dataset.

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Section: Introductionmentioning

confidence: 99%

A New Cluster Validity Index for Fuzzy Clustering Using Separation and Compactness

Chen¹,

Zhong²,

Chen

et al. 2023

Preprint

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Extraction of vehicle behaviors at intersections

De Moura,

Nashashibi

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Optimization-Assisting Dual-Step Clustering of Time Series Data

Rajesh¹,

Seetha²

2022

International Journal of Distributed Systems and Technologies

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This paper aims to propose a new time series data clustering with the following steps: (1) data reduction and (2) clustering. The main objective of the time series data clustering is to minimize the dataset size via a prototype defined for same time series data in every group that significantly reduced the complexities. Initially, the time series dataset in the data reduction step is subjected to preprocessing process. Further, in the proposed probability based distance measure evaluation, the time series data is grouped into subclusters. In the clustering step, the proposed shape based similarity measure is performed. Moreover, the clustering process is carried out by optimized k-mean clustering in which the center point is optimally tuned by a new customized whale optimization algorithm (CWOA). At last, the performance of the adopted model is computed to other traditional models with respect to various measures such as sensitivity, accuracy, FPR, conentropy, precision, FNR, specificity, MCC, entropy, F-measure, and Rand index, respectively.

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Temporal gap statistic: A new internal index to validate time series clustering

Cited by 3 publications

References 24 publications

A New Cluster Validity Index for Fuzzy Clustering Using Separation and Compactness

A New Cluster Validity Index for Fuzzy Clustering Using Separation and Compactness

Extraction of vehicle behaviors at intersections

Optimization-Assisting Dual-Step Clustering of Time Series Data

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