The Community for Technology Leaders
RSS Icon
Issue No.04 - April (2012 vol.24)
pp: 759-768
Zhiwen Yu , Northwestern Polytechnical University, Xi An
Zhiyong Yu , Fuzhou University, Fuzhou
Xingshe Zhou , Northwestern Polytechnical University, Xi An
Christian Becker , Mannheim University, Mannheim
Yuichi Nakamura , Kyoto University, Kyoto
Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Tree-based interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.
Human interaction, interaction flow, interaction pattern, meeting, tree-based mining.
Zhiwen Yu, Zhiyong Yu, Xingshe Zhou, Christian Becker, Yuichi Nakamura, "Tree-Based Mining for Discovering Patterns of Human Interaction in Meetings", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 4, pp. 759-768, April 2012, doi:10.1109/TKDE.2010.224
[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] Q. Yang and X. Wu, "10 Challenging Problems in Data Mining Research," Int'l J. Information Technology and Decision Making, vol. 5, no. 4, pp. 597-604, 2006.
[3] 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.
[4] 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.
[5] W. Geyer, H. Richter, and G.D. Abowd, "Towards a Smarter Meeting Record—Capture and Access of Meetings Revisited," Multimedia Tools and Applications, vol. 27, no. 3, pp. 393-410, 2005.
[6] P. Chiu, A. Kapuskar, S. Reitmeier, and L. Wilcox, "Room with a Rear View: Meeting Capture in a Multimedia Conference Room," IEEE Multimedia, vol. 7, no. 4, pp. 48-54, Oct.-Dec. 2000.
[7] Z. Yu, M. Ozeki, Y. Fujii, and Y. Nakamura, "Towards Smart Meeting: Enabling Technologies and a Real-World Application," Proc. Int'l Conf. Multimodal Interfaces (ICMI '07), pp. 86-93, 2007.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] A. Nijholt, R.J. Rienks, J. Zwiers, and D. Reidsma, "Online and Off-Line Visualization of Meeting Information and Meeting Support," The Visual Computer: Int'l J. Computer Graphics, vol. 22, no. 12, pp. 965-976, 2006.
[13] 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.
[14] J.M. DiMicco, K.J. Hollenbach, A. Pandolfo, and W. Bender, "The Impact of Increased Awareness while Face-to-Face," Human-Computer Interaction, vol. 22, no. 1, pp. 47-96, 2007.
[15] R. Bakeman and J.M. Gottman, Observing Interaction: An Introduction to Sequential Analysis. Cambridge Univ. Press, 1997.
[16] 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.
[17] L. Anolli, S. DuncanJr, 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.
[18] K. Gudberg, M. Jonsson, T. Anguera, P. Sánchez-Algarra, C. Olivera, J. Ssantos, J. Campanico, M. Castañer, C. Torrents, M. Dinušová, J. Chaverri, O. Camerino, and M.S. Magnusson, "Application of T-Pattern Detection and Analysis in Sport Research," Open Sports Sciences J., vol. 3, pp. 95-104, 2009.
[19] G. Casas-Garriga, "Discovering Unbounded Episodes in Sequential Data," Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD '03), pp. 83-94, 2003.
[20] T. Morita, Y. Hirano, Y. Sumi, S. Kajita, and K. Mase, "A Pattern Mining Method for Interpretation of Interaction," Proc. Int'l Conf. Multimodal Interfaces (ICMI '05), pp. 267-273, 2005.
[21] Y. Sawamoto, Y. Koyama, Y. Hirano, S. Kajita, K. Mase, K. Katsuyama, K. Yamauchi, "Extraction of Important Interactions in Medical Interviews Using Nonverbal Information," Proc. Int'l Conf. Multimodal Interfaces (ICMI '07), pp. 82-85, 2007.
[22] Y. Liu, L. Chen, J. Pei, Q. Chen, and Y. Zhao, "Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays," Proc. Fifth IEEE Int'l Conf. Pervasive Computing and Comm. (PerCom '07), pp. 37-46, 2007.
[23] H. Cao, N. Mamoulis, and D.W. Cheung, "Mining Frequent Spatio-Temporal Sequential Patterns," Proc. Fifth IEEE Int'l Conf. Data Mining (ICDM '05), pp. 82-89, 2005.
[24] L. Cao, Y. Zhao, and C. Zhang, "Mining Impact-Targeted Activity Patterns in Imbalanced Data," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 8, pp. 1053-1065, Aug. 2008.
[25] D. Perera, J. Kay, I. Koprinska, K. Yacef, and O.R. Zaiane, "Clustering and Sequential Pattern Mining of Online Collaborative Learning Data," IEEE Trans. Knowledge and Data Eng., vol. 21, no. 6, pp. 759-772, June 2009.
[26] H. Tomobe and K. Nagao, "Discussion Ontology: Knowledge Discovery from Human Activities in Meetings," Proc. 20th Ann. Conf. New Frontiers in Artificial Intelligence (JSAI '06), pp. 33-41, 2006.
[27] M. Araki, T. Itoh, T. Kumagai, and M. Ishizaki, "Proposal of a Standard Utterance-Unit Tagging Scheme," J. Japanese Soc. for Artificial Intelligence, vol. 14, no. 2, pp. 251-260, 1999.
[28] Z.W. Yu, Z.Y. Yu, Y. Ko, X. Zhou, and Y. Nakamura, "Inferring Human Interactions in Meetings: A Multimodal Approach," Proc. Sixth Int'l Conf. Ubiquitous Intelligence and Computing (UIC '09), pp. 14-24, July 2009.
[29] M. Kipp, "Anvil—A Generic Annotation Tool for Multimodal Dialogue," Proc. European Conf. Speech Comm. and Technology (Eurospeech '01), pp. 1367-1370, 2001.
[30] D. Chakrabarti and C. Faloutsos, "Graph Mining: Laws, Generators, and Algorithms," ACM Computing Surveys, vol. 38, no. 1,article 2, 2006.
[31] R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proc. 20th Int'l Conf. Very Large Databases (VLDB '94), pp. 487-499, 1994.
[32] D. Gatica-Perez, I. McCowan, D. Zhang, and S. Bengio, "Detecting Group Interest-Level in Meetings," Proc. IEEE Int'l Conf. Acoustic, Speech, and Signal Processing, vol. 1, pp. 489-492, 2005.
[33] R. Agrawal, T. Imielinski, and A. Swami, "Mining Association Rules between Sets of Items in Large Databases," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 207-216, 1993.
4 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool