An Efficient Interaction Pattern Discovery For Human Meetings

International Journal of Computer Trends and Technology (IJCTT)          
© - May Issue 2013 by IJCTT Journal
Volume-4 Issue-5                           
Year of Publication : 2013
Authors :A.Nandha Kumar, N.Baskar


A.Nandha Kumar, N.Baskar"An Efficient Interaction Pattern Discovery For Human Meetings "International Journal of Computer Trends and Technology (IJCTT),V4(5):1269-1276 May Issue 2013 .ISSN Published by Seventh Sense Research Group.

Abstract: - : Meetings are an important communication and coordination activity of teams: status is discussed, new decisions are made, alternatives are considered, details are explained, information is presented, and new ideas are generated. As such, meetings contain a large amount of rich project information that is often not formally documented. Capturing all of this informal meeting information has been a topic of research in several communities over the past decade. In this work, data mining techniques are used to detect and analyze the frequent interaction patterns to discover various types of knowledge on human interactions. An interaction tree based pattern mining algorithms was proposed to analyze tree structures and extract interaction flow patterns for meetings. In this work tree based mining algorithm proposed for human interaction flow, where the human interaction flow in a discussion session is represented as a tree. Proposed system extend an interactive tree based pattern mining algorithm in two ways. First, it is proposed a mining method to extract frequent patterns of human interaction to support several categories of meeting. Second, it is explored modified embedded tree mining for hidden interaction pattern discovery. Modified Embedded subtree mining is the generalization of induced subtrees, which not allow direct parent child branches, also considers ancestor-descendant branches. The experimental results show the discovered patterns can be utilized to evaluate a meeting discussion (debate) is efficient and compare the results of different algorithms of interaction flow.


[1] Z.W. Yu, Z.Y. Yu, H. Aoyama, M. Ozeki, and Y. Nakamura, “Capture, Recognition, and Visualization of Human Semantic Interactions in Meetings,” Proc. Eighth IEEE Int’l Conf. Pervasive Computing and Comm. (PerCom ’10), pp. 107-115, Mar.-Apr. 2010.
[2] M.J. Zaki, “Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 8, pp. 1021-1035, Aug. 2005.
[3] C. Wang, M. Hong, J. Pei, H. Zhou, W. Wang, and B. Shi, “Efficient Pattern-Growth Methods for Frequent Tree Pattern Mining,” Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD ’04), pp. 441-451, 2004.
[4] S. Junuzovic, R. Hegde, Z. Zhang, P. Chou, Z. Liu, and C. Zhang, “Requirements and Recommendations for an Enhanced Meeting Viewing Experience,” Proc. ACM Int’l Conf. Multimedia, pp. 539- 548, 2008.
[5] Z. Yu and Y. Nakamura, “Smart Meeting Systems: A Survey of State-of-the-Art and Open Issues,” ACM Computing Surveys, vol. 42, no. 2, article 8, Feb. 2010.
[6] R. Stiefelhagen, J. Yang, and A. Waibel, “Modeling Focus of Attention for Meeting Indexing Based on Multiple Cues,” IEEE Trans. Neural Networks, vol. 13, no. 4, pp. 928-938, July 2002.
[7] I. Mccowan, D. Gatica-Perez, S. Bengio, G. Lathoud, M. Barnard, and D. Zhang, “Automatic Analysis of Multimodal Group Actions in Meetings,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 305-317, Mar. 2005.
[8] K. Otsuka, H. Sawada, and J. Yamato, “Automatic Inference of Cross-Modal Nonverbal Interactions in Multiparty Conversations,” Proc. Int’l Conf. Multimodal Interfaces (ICMI ’07), pp. 255- 262, 2007.
[9] M.S. Magnusson, “Discovering Hidden Time Patterns in Behavior: T-Patterns and Their Detection,” Behavior Research Methods, Instruments and Computers, vol. 32, no. 1, pp. 93-110, 2000.
[10] L. Anolli, S. Duncan Jr, M.S. Magnusson, and G. Riva, “The Hidden Structure of Interaction: From Neurons to Culture Patterns,” Emerging Communication: Studies in New Technologies and Practices in Communication, IOS Press, Apr. 2005.

Keywords — Tree based mining, Frequent interaction subtree mining, Frequent interaction mining and Modified Embedded subtree mining.