International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 8, No. 4, 8 Apr. 2016
Cover page and Table of Contents: PDF (size: 592KB)
Web Video Object Mining: A Novel Approach for Knowledge Discovery
Full Text (PDF, 592KB), PP.67-75
Views: 0 Downloads: 0
Author(s)
Siddu P. Algur
1
Prashant Bhat
1,*
Index Terms
Meta-objects, Web Videos, Clustering, YouTube, Expectation Maximization, Distribution Based Clusters
Abstract
The impact of social Medias such as YouTube, Twitter, and FaceBook etc on the modern world is led to huge growth in the size of video data over the cloud and web. The evolution of smart phones/Tabs could be one of the reasons for increasing in the rate of huge video data over the web. Due to the rapid evolution of web videos over the web, it is becoming difficult to identify popular, non-popular and average popular videos without watching the content of it. To cluster web videos based on their metadata into ‘Popular’, ‘Non-Popular’, and ‘Average Popular’ is one of the complex research questions for the Social Media and Computer Science researchers’. In this work, we propose two effective methods to cluster web videos based on their meta-objects. Large scale web video meta-objects such as- length, view counts, numbers of comments, rating information are considered for knowledge discovery process. The two clustering algorithms-Expectation Maximization (EM) and Distribution Based (DB) clustering are used to form three types of clusters. The resultant clusters are analyzed to find popular video cluster, average popular video cluster and non-popular video clusters. And also the results of EM and DB clusters are compared as a step in the process of knowledge discovery.
Cite This Paper
Siddu P. Algur, Prashant Bhat, "Web Video Object Mining: A Novel Approach for Knowledge Discovery", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.4, pp.67-75, 2016. DOI:10.5815/ijisa.2016.04.08
Reference
[1]Statistics of YouTube in official website: www.youtube.com
[2]Siddu P. Algur, Prashant Bhat, Suraj Jain, “Metadata Construction Model for Web Videos: A Domain Specific Approach”, International Journal of Engineering and Computer Science, December 2014.
[3]Siddu P. Algur, Prashant Bhat, “Metadata Based Classification and Analysis of Large Scale Web Videos”, International Journal of Emerging Trends and Technologies in Computer Science, May-June 2015
[4]Siddu P Algur, Prashant Bhat, “Web Video Object Mining: Expectation Maximization and Distribution Based Clustering of Web Video Metadata Objects”, International Journal of Information Engineering and Electronic Business, MECS Publishers Volume 8, N1, January 2016.
[5]Dataset for "Statistics and Social Network of YouTube Videos", Simon Fraser University: http://netsg.cs.sfu.ca/youtubedata/
[6]Siddu P. Algur, Prashant Bhat, Suraj Jain, “The Role of Metadata in Web Video Mining: Issues and Perspectives”, International Journal of Engineering Sciences & Research Technology, February-2015.
[7]Chirag Shah, Charles File, “Infoextractor – A Tool for Social Media Data Mining”, JITP 2011.
[8]Siddu P Algur, Prashant Bhat, “Web Video Mining: Metadata Predictive Analysis Using Classification Techniques”, International Journal of Information Technology and Computer Science, MECS publishers, [Accepted, September 2015].
[9]Renjie Zhou, Samamon Khemmarat, Lixin Gao, “The Impact of YouTube Recommendation System on Video”, ACM 978-1-4503-0057-5/10/11, November 2010.
[10]Jose San Pedro, Stefan Siersdorfer, and Mark S, “Content Redundancy in YouTube and its Application to Video Tagging, ACM Transactions, Online Reference: http://www.l3s.de/~siersdorfer/sources/2011/TOIS-2011-youtube%28pre-print%29.pdf
[11]C.F-Hsu, James C., and E.Khabiri, “Hierarchical Comment Based Clustering”, ACM 978-1-4503-0113-8/11/03, March 2011.
[12]Xu Cheng, Cameron Dale, and Jiangchuan Liu, “Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study”, arXiv:0707.3670v1 [cs.NI] 25 Jul 2007.
[13]C. Ramachandran, R.Malik, Xin Jin and Jing Gao “VideoMule: A Consensus Learning Approach to Multi-Label Classification from Noisy User-Generated Videos”, ACM, MM’09, October 19–24, 2009
[14]Alex Hindle, Jie Shao Dan Lin, Jiaheng Lu and Rui Zhang “Clustering Web Video Search Results based on Integration of Multiple Features”, World Wide Web, Springer, 2011.
[15]J.S.Pedro, Stefan Siersdorfer and Mark Sanderson, “Content Redundancy in YouTube and its Application to Video Tagging”, ACM Transactions on Information Systems, March 2011.