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Social Web Videos Clustering Based on Ensemble Technique

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9920))

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Abstract

Currently, a massive amount of videos has become a challenging research area for social web videos mining. Clustering ensemble is a common approach to clustering problems, which combine a collection of clusterings into a superior solution. Textual features are widely used to describe a web video. Whereas, local and global features also have their own advantages to describe a web video as well. So we extract the local and global features as we called low-level/semantic features and high-level/visual features respectively to help to better describe a main source. In this paper, we propose a combining function of three similarity models to enhance the similarity values of videos, and then present a framework for Clustering Ensemble with the support of Must-Link constraint (CE-ML) to formulate in ensembling for clustering purposes. Experimental evaluation on the real world social web video has been performed to validate the proposed framework.

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Notes

  1. 1.

    http://www.youtube.com.

  2. 2.

    http://vireo.cs.cityu.edu.hk/research/vireo374/.

  3. 3.

    www.ranks.nl/stopwords.

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Acknowledgements

This work is supported by the National Science Foundation of China (No. 61573292).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Vinath Mekthanavanh & Tianrui Li

Authors
  1. Vinath Mekthanavanh
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  2. Tianrui Li
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Corresponding author

Correspondence to Tianrui Li .

Editor information

Editors and Affiliations

  1. Universidad Católica del Norte, Antofagasta, Chile

    Víctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del Católica del Norte, Antofagasta, Chile

    Claudio Meneses

  5. Tongji University, Shanghai, China

    Duoqian Miao

  6. University of Applied Sciences, Munich, Germany

    Georg Peters

  7. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  8. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  9. Universidad de Chile, Santiago, Chile

    Richard Weber

  10. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

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Mekthanavanh, V., Li, T. (2016). Social Web Videos Clustering Based on Ensemble Technique. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_41

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  • DOI: https://doi.org/10.1007/978-3-319-47160-0_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47159-4

  • Online ISBN: 978-3-319-47160-0

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