Research Article | OPEN ACCESS
Temporal Semantic Analysis Based Human Interaction Pattern Mining using Partial Ancestral Graph
1S. Uma and 2J. Suguna
1School of Computer Science, CMS College of Science and Commerce, Coimbatore, India
2Depertment of Computer Science, Vellalar College for Women, Erode, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2014 12:1487-1491
Received: August 03, 2014 | Accepted: September 14, 2014 | Published: September 25, 2014
Abstract
In modern life, interactions between human beings occur frequently in meeting discussions. Semantic knowledge of meetings can be revealed by discovering interaction patterns from those meetings. Human interaction flow in a discussion session is used to extract the frequent pattern interaction. In this study Partial Ancestral Graph (PAG) meet method is proposed to mine frequent interaction among patterns. The experimental results shows that the proposed method can 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.
Keywords:
Directed Acyclic Graph (DAG), human interactions , Partial Ancestral Graph (PAG) , tree based mining,
References
-
Anolli, L., S. Duncan Jr., M.S. Magnusson and G. Riva, 2005. The Hidden Structure of Interaction: From Neurons to Culture Patterns. Emerging Communication: Studies in New Technologies and Practices in Communication, IOS Press, Apr. 2005.
-
Bakeman, R. and J.M. Gottman, 1997. Observing Interaction: An Introduction to Sequential Analysis. 2nd Edn., Cambridge Univ. Press, New York.
CrossRef
-
Barnard, M., S. Bengio, D. Gatica-Perez, G. Lathoud, I. Mccowan and D. Zhang, 2005. Automatic analysis of multimodal group actions in meetings. IEEE T. Pattern Anal., 27(3): 305-317.
CrossRef PMid:15747787
-
Becker, C. and Z. Yu, 2012. Tree-based mining for discovering patterns of human interaction in meetings. Proceeding of 8th IEEE International Conferences on Knowledge Discovery and Data Mining (PAKDD'10), pp: 107-115.
PMid:22663011
-
Casas-Garriga, G., 2003. Discovering nbounded episodes in sequential data. Proceeding of European Conferences on Principles and Practice of Knowledge Discovery in Databases (PKDD "03), pp: 83-94.
-
Karthika, D. and R. RangaRaj, 2013. A graph-based interaction pattern discovery for human meetings. Int. J. Adv. Comput. Sci. Technol., 2(8).
-
Magnusson, M.S., 2000. Discovering hidden time patterns in behavior: T-patterns and their detection. Behav. Res. Meth. Ins. C., 32(1): 93-110.
CrossRef PMid:10758668
-
McCowan, I., D. Gatica-Perez, S. Bengio, G. Lathoud, M. Barnard and D. Zhang, 2005. Automatic analysis of multimodal group actions in meetings. IEEE T. Pattern. Anal., 27(3): 305-317.
-
Morita, T., Y. Hirano, Y. Sumi, S. Kajita and K. Mase, 2005. A pattern mining method for Interpretation of interaction. Proceeding of International Conferences on Multimodal Interfaces (ICMI "05), pp: 267-273.
CrossRef
-
Nandha Kumar, A. and N. Baskar, 2013. An efficient interaction pattern discovery for human meetings. Int. J. Comput. Trends Technol. (IJCTT), 4(5).
-
Otsuka, K., H. Sawada and J. Yamato, 2007. Automatic inference of cross-modal nonverbal interactions in multiparty conversations: "who responds to whom, when, and how?" from gaze, head gestures, and utterances. Proceedings of the 9th International Conference on Multimodal Interfaces (ICMI, 2007). ACM Press, New York, pp: 255-262.
-
Uma, S. and J. Suguna, 2013. Tree-based weighted interesting pattern mining approach for human interaction pattern discovery. Int. Rev. Comput. Software (IRECOS), 8(11): 2570-2575.
-
Waibel, A., M. Bett, M. Finke and R. Stiefelhagen, 1998. Meeting browser: Tracking and summarizing meetings. Proceeding of DARPA Broadcast News Transcription and Understanding Workshop, pp: 281-286.
-
Werth, T., A. Dreweke, M. Wörlein, I. Fischer and M. Philippsen, 2008. DAGMA: Mining directed acyclic graphs. Proceeding of IADIS European Conference on Data Mining 2008, pp: 11-18.
PMid:18489803
-
Yang, Q. and X. Wu, 2006. 10 challenging problems in data mining research. Int. J. Inf. Tech. Decis., 5(4): 597-604.
CrossRef
-
Yu, Z., Z. Yu, Y. Ko, X. Zhou and Y. Nakamura, 2009. Inferring human interactions in meetings: A multimodal approach. In: Zhang, D. et al. (Eds.), UIC 2009. LNCS 5582, Springer-Verlag, Berlin, Heidelberg, pp: 14-24.
CrossRef
-
Yu, Z., Z. Yu, X. Zhou, C. Becker and Y. Nakamura, 2012. Tree-based mining for discovering patterns of human interaction in meetings. IEEE T. Knowl. Data En., 24(4): 759-768.
CrossRef
-
Yu, Z.W., Z.Y. Yu, H. Aoyama, M. Ozeki and Y. Nakamura, 2010. Capture, recognition, and visualization of human semantic interactions in meetings. Proceeding of 8th IEEE International Conferences on Pervasive Computing and Communications (PerCom '10), pp: 107-115.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|