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Multimedia Tools and Applications

, Volume 78, Issue 14, pp 20333–20360 | Cite as

A soft clustering technique with layered feature extraction for social image mining

  • Seema WazarkarEmail author
  • Bettahally N. Keshavamurthy
Article
  • 61 Downloads

Abstract

Social image mining is beneficial to accomplish tasks like event detection, suspicious activity detection, prediction of future trends, identification of mentally depressed people, etc. To carry out social image mining, data mining techniques need to be used. Clustering is one of the most important tasks of data mining which is able to deal with the unlabelled data. But, less number of clustering approaches are having ability to handle the uncertain image data. Thus, in this paper we proposed a soft clustering algorithm named as ROugh Mean Shift clustering (ROMS) with layered feature extraction model for social images. Effectiveness of the rough set theory and mean shift concepts are incorporated in this algorithm. It makes the ROMS to deal with the vagueness and the automatic determination of cluster numbers in given data. Proposed method is experimented on three datasets- synthetic, standard and real-world datasets and compared with existing techniques. Experimental results show that ROMS performs better as compared to other techniques.

Keywords

Social image mining Feature extraction Rough set theory Mean shift clustering 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Institute of Technology GoaPondaIndia

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