Abstract
The oceans of data generated by social media have become a goldmine to researchers in the data mining domain. Discovering actionable knowledge by extracting latent patterns has many advantages. One of the utilities of mining social data is learning collective behavior which helps in taking well informed decisions pertaining to humanitarian assistance, disaster relief and such real world applications. In multi mode while studying the collective behavior using edge centric approach, object heterogeneity is a problem. In this paper, we propose a scheme temporal regularized evolutionary multimode clustering algorithm which can address object heterogeneity in social media with multi-mode more effectively. With this the prediction performance of collective behavior is improved further. We built a prototype application to demonstrate the proof of perception. The empirical results are encouraging and our approach can be used in real world applications that mine social media data explicitly.
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Totad, S.G., Smitha Kranthi, A., Matta, A.K. (2015). Addressing Object Heterogeneity Through Edge Cluster in Multi-mode Networks. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_27
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DOI: https://doi.org/10.1007/978-81-322-2205-7_27
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