Recognition of emergent human behaviour in a smart home: A data mining approach

Pervasive and Mobile Computing

Volumn 3, Issue 2, 2007, Pages 95-116

  Lühr, Sebastian ^a   West, Geoff ^a   Venkatesh, Svetha ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

Emergent behaviour; Intertransaction association rules; Smart homes; Visual data mining

Indexed keywords

ALGORITHMS; CONGESTION CONTROL (COMMUNICATION); DATA MINING; INTELLIGENT AGENTS; INTELLIGENT STRUCTURES; INTERFACES (COMPUTER);

EMERGENT BEHAVIOR; INTERTRANSACTION ASSOCIATION RULES; SMART HOMES; VISUAL DATA MINING;

BEHAVIORAL RESEARCH;

EID: 33847284635     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2006.08.002     Document Type: Article

References (26)

1. United Nations, World population ageing: 1950-2050 (2002)
2. E. Mynatt, I. Essa, W. Rogers, Increasing the opportunities for aging in place, in: Proc. 2000 Conf. Universal Usability, 2000, pp. 65-71
3. Lu H., Feng L., and Han J. Beyond intra-transaction association analysis: Mining multi-dimensional inter-transaction association rules. ACM Transactions on Information Systems 18 4 (2000) 423-454
4. Tung A.K.H., Lu H., Han J., and Feng L. Efficient mining of intertransaction association rules. IEEE Knowledge and Data Engineering 15 1 (2003) 43-56
5. S. Lühr, G. West, S. Venkatesh, An extended frequent pattern tree for intertransaction association rule mining, Tech. Rep., Department of Computing, Curtin University of Technology, March 2005
6. Han J., Pei J., Yin Y., and Mao R. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8 1 (2004) 53-87
7. D.J. Cook, M. Youngblood, E.O. Heierman, III, K. Gopalratnam, S. Rao, A. Litvin, F. Khawaja, MavHome: An agent-based smart home, in: IEEE Int'l Conf. Pervasive Computing and Communications, 2003, pp. 521-524
8. M.C. Mozer, Lessons from an adaptive house, in: D. Cook, S. Das (Eds.), Smart Environments: Technology, Protocols and Applications, 2004, pp. 273-294
9. C.D. Kidd, R. Orr, G.D. Abowd, C.G. Atkeson, I.A. Essa, B. MacIntyre, E.D. Mynatt, T. Starner, W. Newstetter, The aware home: A living laboratory for ubiquitous computing research, in: Int'l Workshop Cooperative Buildings. Integrating Information, Organizations, and Architecture, 1999, pp. 191-198
10. S.S. Intille, K. Larson, J.S. Beaudin, J. Nawyn, E.M. Tapia, P. Kaushik, A living laboratory for the design and evaluation of ubiquitous computing technologies, in: Conf. Human Factors in Computing Systems, 2005, pp. 1941-1944
11. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in: Proc. 1993 ACM SIGMOD Int'l Conf. Management of Data, 1993, pp. 207-216
12. H. Mannila, H. Toivonen, A.I. Verkamo, Discovering frequent episodes in sequences, in: Proc. Int'l Conf. Knowledge Discovery and Data Mining, Montréal, Canada, 1995, pp. 210-215
13. R. Agrawal, R. Srikant, Mining sequential patterns, in: Proc. Eleventh Int'l Conf. Data Engineering, 1995, pp. 3-14
14. H. Lu, J. Han, L. Feng, Stock movement prediction and n-dimensional inter-transaction association rules, in: Proc. 1998 SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1998, pp. 12:1-12:7
15. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proc. 20th Int'l Conf. Very Large Data Bases, 1994, pp. 487-499
16. G. Dong, J. Li, Efficient mining of emerging patterns: Discovering trends and differences, in: ACM Int'l Conf. Knowledge Discovery and Data Mining, 1999, pp. 43-52
17. G. Dong, J. Li, X. Zhang, Discovering jumping emerging patterns and experiments on real datasets, in: Int'l Database Conf. Heterogeneous and Internet Databases, 1999, pp. 155-168
18. de Oliveira M.C.F., and Levkowitz H. From visual data exploration to visual data mining: A survey. Visualization and Computer Graphics 9 3 (2003) 378-394
19. C. Brunk, J. Kelly, R. Kohavi, MineSet: An integrated system for data mining, in: Proc. Int'l Conf. Knowledge Discovery and Data Mining, 1997, pp. 135-138
20. R. Agrawal, M. Mehia, J. Shafer, R. Srikant, A. Arning, T. Bollinger, The quest data mining system, in: Proc. Second Int'l Conf. Knowledge Discovery and Data Mining, 1996, pp. 244-249
21. P.C. Wong, P. Whitney, J. Thomas, Visualizing association rules for text mining, in: IEEE Sym. Information Visualization, 1999, pp. 120-123
22. K.-H. Ong, K.-L. Ong, W.-K. Ng, E.-P. Lim, Crystalclear: Active visualization of association rules, in: ICDM 2002 Workshop on Active Mining, 2002
23. E.M. Tapia, S.S. Intille, K. Larson, Activity recognition in the home using simple and ubiquitous sensors, in: Int'l Conf. Pervasive Computing, 2004, pp. 158-175
24. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: Proc. 2000 ACM SIGMOD Int'l Conf. Management of Data, 2000, pp. 1-12
25. S. Lühr, S. Venkatesh, G. West, Emergent intertransaction association rules for abnormality detection in intelligent environments, in: Int'l Conf. Intelligent Sensors, Sensor Networks and Information Processing, 2005, pp. 343-347
26. R. Srikant, Q. Vu, R. Agrawal, Mining association rules with item constraints, in: Proc. Int'l Conf. Knowledge Discovery and Data Mining, 1997, pp. 67-73