Unsupervised clustering of symbol strings and context recognition

The representation of information based on symbol strings has been applied to the recognition of context. A framework for approaching the context recognition problem has been described and interpreted in terms of symbol string recognition. The symbol string clustering map (SCM) is introduced as an efficient algorithm for the unsupervised clustering and recognition of symbol string data. The SCM can be implemented in an online manner using a computationally simple similarity measure based on a weighted average. It is shown how measured sensor data can be processed by the SCM algorithm to learn, represent and distinguish different user contexts without any user input.

[1]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  Johan Himberg,et al.  Collaborative context determination to support mobile terminal applications , 2002, IEEE Wirel. Commun..

[3]  Johan Himberg,et al.  Collaborative context recognition for handheld devices , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[4]  Tapio Seppänen,et al.  Hand gesture recognition of a mobile device user , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[5]  Alex Pentland,et al.  Extracting context from environmental audio , 1998, Digest of Papers. Second International Symposium on Wearable Computers (Cat. No.98EX215).

[6]  Albrecht Schmidt,et al.  Advanced Interaction in Context , 1999, HUC.

[7]  Heikki Mannila,et al.  Time series segmentation for context recognition in mobile devices , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[8]  Tapio Seppänen,et al.  Adapting applications in handheld devices using fuzzy context information , 2003, Interact. Comput..

[9]  Kristof Van Laerhoven,et al.  What shall we teach our pants? , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[10]  Padhraic Smyth,et al.  A General Probabilistic Framework for Clustering Individuals , 2000, KDD 2000.

[11]  Philip S. Yu,et al.  Fast algorithms for projected clustering , 1999, SIGMOD '99.

[12]  Lei Liu,et al.  MobiMine: monitoring the stock market from a PDA , 2002, SKDD.

[13]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[14]  Johan Himberg,et al.  A hierarchical approach to learning context and facilitating user interaction in mobile devices , 2003 .

[15]  Heikki Mannila,et al.  Extracting the Context of a Mobile Device User , 2001 .

[16]  Alex Pentland,et al.  Recognizing user context via wearable sensors , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[17]  Padhraic Smyth,et al.  A general probabilistic framework for clustering individuals and objects , 2000, KDD '00.

[18]  Urpo Tuomela,et al.  Context studio: Tool for personalizing context-aware applications in mobile terminals , 2003 .

[19]  Jani Mäntyjärvi,et al.  Sensor-based context recognition for mobile applications , 2003 .

[20]  J. Himberg,et al.  Using PCA and ICA for exploratory data analysis in situation awareness , 2001, Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 (Cat. No.01TH8590).

[21]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[22]  Johan Himberg,et al.  Towards Context Awareness Using Symbol Clustering Map , 2003 .