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Clustering Method for Predicting Actions of a Human Being at Different Locations

Navitha Varghese, G. Naveen Sundar

Abstract


Human behavior can be characterized by a set of sequential action patterns. As such, there can be a causal relationship among actions. When these actions take place in uncertain conditions, it is difficult to predict the next action based on the observed actions. Thus, it would be a challenging task to establish some causal relationships among the sequential actions under observation .Some techniques are used for labeling actions which also deals with predicting actions. Further, we want to point out potential pitfalls as well as challenging issues of different techniques. We believe that the results of this evaluation will help for building up an autonomous system.

Keywords


Prediction, HMM, CRF, AIBFC,Human behavior

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References


Sira Panduranga Rao and Diane J. Cook,‖ Improving the Performance of Action Prediction through identification of tasks‖ Introduction to Statistical Relational Learning, MIT Press, 2007.

Sang Wan Lee,Yong Soo Kim, and Zeungnam Bien, ―A Nonsupervised Learning Framework of Human Behavior Patterns Based on Sequential Actions‖ IEEE ,knowl-edge and data engineering , VOL. 22, NO. 4, APRIL 2010

J. Lafferty, A. McCallum, and F. Pereira, ―Conditional Random Fields:Probabilistic Models for Segmenting and Labeling Sequence Data,‖ Proc. Int’l Conf. Machine learning,pp.282-289,2002

H.-E. Lee, K.-H. Park, and Z.Z. Bien, ―Iterative Fuzzy Clusterin Algorithm with Supervision to Construct Probabilistic Fuzzy Rule Base from Numerical Data,‖ IEEE Trans. Fuzzy Systems, vol. 16 no. 1, pp. 263-277, Feb. 2008.

M.A. Feki, S.W. Lee, M. Mokhtari, and Z. Bien, ―Context Aware Life Pattern Prediction Using Fuzzy-State Q-Learning,‖ Proc. Fifth Int’l Conf. Smart Home and telecom,2007

J.C.H.W. Christopher and D. Peter, ―Q-Learning,‖ Machin Learning, vol. 8, pp. 279-292, 2002.

M. Philipose et al., ―Inferring Activities from Interactions with Pervasive computing Objects ― pp. 50-57, 2004.

D.H. Wilson and C. Atkeson, ―Simultaneous Tracking and Activity) Sensors,‖ Proc. Pervasive Computing, 2005.

J.-H. Choi et al ―A Technology of Tracking Activities of the Age Healthcare,‖ Kor Information Processing no. 1, pp. 34-43, Jan. 2008.

S. Kubo et al., ―Structural Equation Modeling for Comfort an Thermal Sensation,‖ J. Japan Fuzzy Theory And Intelligent Agent Informatics, vol. 20, no. 2, pp. 164-170, Apr. 2008.

M. Sasajima et al., ―Toward Task-Oriented Mobile Internet Service Navigation—Ontology Based on user modeling in Daily Life,‖ J. Japan Soc. for Fuzzy Theory and Intelligent, vol 1,ppt-36-48,jan 2008

G. Kawakami et al., ―Everyday Life Behavior Monitoring Based on Spatio – Temporal Expansion of Location - EMG Sensor,‖ J. Japan Soc. For Fuzzy Theory and Intelligent Informatics, vol. 20, no. 2, pp. 190-200, Apr. 2008

T. Tajima et al., ―Development of a Marketing System for Recognizing Customer Buying Behavior Sensor,‖ J. Japan Soc. for Fuzzy Theory and Intelligent Informatics, vol. 20, no. vol 5,pp 18-22,apr.2007

Z. Bien and M.-G. Chun, ―A Fuzzy Petri Net Model,‖ Handbook of Fuzzy Computation, C2.4, IOP Publishing Ltd., 1998.

N. Chowdhury and C.A. Murthy, ―Minimum Spanning Tree Based - Technique: Relationship with Bayes’ Classifier,‖ Pattern Recognition, vol. 30, no. 11, 1929, 1997.

M. Lazlo and S. Mukherjee, ―Minimum Spanning Tree Partitioning Algorithm for902 Microaggregation,‖ IEEE Transactions on Data and Knowledge Engineering, vol. 17, no. 7, pp. 911, July 2005.

O. Grygorash, Y. Zhou, and Z. Jorgensen, ―Minimum Spanning Tree Based Clusteri Algorithms,‖Proc. IEEE Int’l Conf. Tools with Artificial Intelligence, pp. 73-81, 2006.

N. R. Pal and J. Biswas, ―Cluster validation using graph theoretic Recognit., vol. 30, no. 6, pp. 847–857.

R. Krishnapuram and J. Keller, ―A possibilistic approach to clustering,‖IEEE Trans. Fuzzy Syst., vol. 1, no. 4, pp. 98–110, 2006.


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