Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties

Hoecker, Maximilian; Polsterer, Kai Lars; Kügler, S. D.; Heuveline, Vincent

doi:10.1109/cse.2015.35

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Then, each point is described on the double logarithmic power spectrum log p (ω)-logω diagram, the straight line is fitted by the least square method, and the fitted straight line slope is K, The frequency domain fractal dimension Dh [11] based on modern power spectrum estimation can be expressed as (6)…”

Section: Feature Extraction Of Leakage Current Signalmentioning

confidence: 99%

Research on Insulator Leakage Current Data Analysis Based on DBClustering

Wang¹,

Lv²,

Zhang³

2016

Proceedings of the 2016 4th International Conference on Electrical &Amp; Electronics Engineering and Computer Science (ICEEECS

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Abstract. In order to analyze the operating condition of the leakage current of the insulator effectively, a cluster structure clustering algorithm based on density named DBClustering is designed. In this paper, four characteristic values of leakage current are extracted from the two aspects of leakage current in time domain and frequency domain, then the leakage current data sample set is formed, after that DBClustering algorithm was used to cluster and analyze this set . By extracting and analyzing the leakage current data collected in the laboratory, corresponding experiments show that the algorithm is superior to the traditional density clustering algorithm in terms of time and space consumption, and it is helpful to analyze the state of leakage current assessment and flashover studies.

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Section: Feature Extraction Of Leakage Current Signalmentioning

confidence: 99%

Research on Insulator Leakage Current Data Analysis Based on DBClustering

Wang¹,

Lv²,

Zhang³

2016

Proceedings of the 2016 4th International Conference on Electrical &Amp; Electronics Engineering and Computer Science (ICEEECS

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“…As a fundamental technology of big data analysis, clustering divides objects into different clusters based on different similarity measures, making objects in the same cluster more similar to other objects in different groups [ 18 , 19 ]. They are commonly used to organize, analyze, communicate, and retrieve tasks [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data

Xiang

Zhang

et al. 2019

Sensors

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Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.

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Anaysis of controller based IEEE 802.11 system with similarity measure clustering: Evaluation of channel allocation efficiency by change detection

Gál

Terdik

2017

2017 5th International Symposium on Digital Forensic and Security (ISDFS)

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Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties

Cited by 3 publications

References 26 publications

Research on Insulator Leakage Current Data Analysis Based on DBClustering

Research on Insulator Leakage Current Data Analysis Based on DBClustering

The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data

Anaysis of controller based IEEE 802.11 system with similarity measure clustering: Evaluation of channel allocation efficiency by change detection

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