research-article

Online frame-based clustering with unknown number of clusters

Authors:

Nasser KehtarnavazAuthors Info & Claims

Pattern Recognition, Volume 57, Issue C

Pages 70 - 83

https://doi.org/10.1016/j.patcog.2016.03.010

Published: 01 September 2016 Publication History

Abstract

This paper presents an online frame-based clustering algorithm (OFC) for unsupervised classification applications in which data are received in a streaming manner as time passes by with the number of clusters being unknown. This algorithm consists of a number of steps including density-based outlier removal, new cluster generation, and cluster update. It is designed for applications when data samples are received in an online manner in frames. Such frames are first passed through an outlier removal step to generate denoised frames with consistent data samples during transitions times between clusters. A classification step is then applied to find whether frames belong to any of existing clusters. When frames do not get matched to any of existing clusters and certain criteria are met, a new cluster is created in real time and in an on-the-fly manner by using support vector domain descriptors. Experiments involving four synthetic and two real datasets are conducted to show the performance of the introduced clustering algorithm in terms of cluster purity and normalized mutual information. Comparison results with similar clustering algorithms designed for streaming data are also reported exhibiting the effectiveness of the introduced online frame-based clustering algorithm. Online frame-based clustering algorithm without having any knowledge of number of clusters.For applications when samples of a class appear in streaming frames.Superior to existing algorithms applicable to online frame-based clustering.

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Cited By

Shukla UNanda S(2019)Dynamic clustering with binary social spider algorithm for streaming datasetSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3627-623:21(10717-10737)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s00500-018-3627-6
Du TQu SWang Q(2018)A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density PeakComplexity10.1155/2018/52325432018Online publication date: 21-Oct-2018
https://dl.acm.org/doi/10.1155/2018/5232543
Dawar NKehtarnavaz N(2017)Continuous detection and recognition of actions of interest among actions of non-interest using a depth camera2017 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2017.8297079(4227-4231)Online publication date: 17-Sep-2017
https://dl.acm.org/doi/10.1109/ICIP.2017.8297079

Online frame-based clustering with unknown number of clusters
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 57, Issue C

September 2016

198 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 September 2016

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Cited By

Shukla UNanda S(2019)Dynamic clustering with binary social spider algorithm for streaming datasetSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3627-623:21(10717-10737)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1007/s00500-018-3627-6
Du TQu SWang Q(2018)A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density PeakComplexity10.1155/2018/52325432018Online publication date: 21-Oct-2018
https://dl.acm.org/doi/10.1155/2018/5232543
Dawar NKehtarnavaz N(2017)Continuous detection and recognition of actions of interest among actions of non-interest using a depth camera2017 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2017.8297079(4227-4231)Online publication date: 17-Sep-2017
https://dl.acm.org/doi/10.1109/ICIP.2017.8297079

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