Unsupervised Clustering of Symbol Strings and Context Recognition

Authors:
John A. Flanagan;Jani Mäntyjarvi;Johan Himberg
Affiliations:
-;-;-
Venue:
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Year:
2002

Citing 0
Cited 10

Collaborative Context Recognition for Handheld Devices

PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Using dynamic time warping for online temporal fusion in multisensor systems

Information Fusion
Efficient algorithms for segmentation of item-set time series

Data Mining and Knowledge Discovery
An ontology for mobile device sensor-based context awareness

CONTEXT'03 Proceedings of the 4th international and interdisciplinary conference on Modeling and using context
An unsupervised learning paradigm for peer-to-peer labeling and naming of locations and contexts

LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
Unsupervised clustering of context data and learning user requirements for a mobile device

CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
An efficient feature selection method for mobile devices with application to activity recognition

Neurocomputing
A self-organizing map for transactional data and the related categorical domain

Applied Soft Computing
An approach to social recommendation for context-aware mobile services

ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Clustering with Proximity Graphs: Exact and Efficient Algorithms

International Journal of Knowledge-Based Organizations

Quantified Score

Hi-index	0.00

Visualization

Abstract

The representation of information based on symbolstrings has been applied to the recognition of context. Aframework for approaching the context recognition problemhas been described and interpreted in terms of symbolstring recognition. The Symbol String Clustering Map(SCM) is introduced as an efficient algorithm for the unsupervisedclustering and recognition of symbol string data.The SCM can be implemented in an on line manner usinga computationally simple similarity measure based ona weighted average. It is shown how measured sensor datacan be processed by the SCM algorithm to learn, representand distinguish different user contexts without any user input.