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A Proposed New Algorithm for Hierarchical Clustering Suitable for Video Data Mining

D. Saravanan, Dr.S. Srinivasan

Abstract


Video clustering is one of the important tasks in video data mining. We discuss about the different video clustering techniques using hierarchical clustering algorithms like CURE, BIRCH, and CHAMLEON. Set of video frames are clustered using the hierarchical clustering algorithms. As a result of comparison of results all the hierarchical algorithms offer good performance for some set of video files only. That is each clustering algorithm shows best clustering on particular video files only. It fails to forms best clusters of all type of video files. Hence we propose a hierarchical clustering algorithm that offers best clustering performance for all type of video files at any circumstances.

Keywords


Clustering Algorithm, Hierarchical Clustering Algorithm, Video Clustering, Video Data mining

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References


S Djerroud, l. Zaoui, H. Abed “ Improvements of content based image retrieval: integration of chameleon method “jig‟2007 – 3èmes journées internationales sur ‟informatique graphique.

Yen-jen oyang, chien-yu chen, shien-ching hwang, and cheng-fang lin “characteristics of a hierarchical data clustering algorithm based on gravity theory “ .

Zhou hao feng, yuan qing qing “fast partition and hierarchy based clustering algorithms” 2003

Shyamprasad chikkerur, vijay sundaram, member, ieee, martin reisslein, and lina j. karam “objective video quality assessment methods: a classification, review, and performance comparison“ 0018-9316/ © 2011 IEEE

Mohamed elasmar prashant thiruvengadachari javier salinas martin “clustering data streams”

Rui Xu Xu, Student Member Member, IEEE and Donald Wunsch unsch II II, Fellow ellow, IEEE “Survey of clustering algorithms” IEEE TRANSA TRANSACTIONS CTIONS ON NEURAL NETW NETWORKS, ORKS, VOL. OL. 16, NO. 3, MA MAY 2005

By Tian Zhang, Raghu Ramakrishnan “Birch: Balanced Iterative Reducing and Clustering using Hierarchies “Vladimir Jelić 3218/10

Chen Yabing (HT00-6078H) Cong Gao ” BIRCH: An Efficient Data Clustering Method for Very Large Databases “Advanced Topics in Database Management

Danzhou Liu, Khan Vu and Keen Ahead,” Fast Query Point Movement Techniques For Large CBIR Systems”, IEEE Transactions On Knowledge And Data Engineering, Vol. 21, No. 5, Page 729-743, May 2009.

Z. Barceló‟s, E. L. Flores; C. A. Z. Barceló‟s, S. F. Silva, M. A. Batista,” A Multi-Dimensional Similarity Modeling and Relevance Feedback Approach for Content-Based Image Retrieval”, Systems, Signals and Image Processing, 2009,Page 1-5.

Sun Yen , Wang Zheng-xuan , Wang Dong-mei “An Image Retrieval Method Based on Relevance Feedback and Collaborative Filtering”, 2010.

Yihun Alemu, Jong-bin Koh, Muhammed Ikram, Dong-Kyoo Kim “Image Retrieval in Multimedia Databases: A survey”, Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing,2009


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