Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Multi-sensor context-awareness in mobile devices and smart artifacts
Mobile Networks and Applications
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
A time-series-based feature extraction approach for prediction of protein structural class
EURASIP Journal on Bioinformatics and Systems Biology
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Human motion analysis and action recognition
MAASE'08 Proceedings of the 1st WSEAS International Conference on Multivariate Analysis and its Application in Science and Engineering
Proceedings of the 6th ACM conference on Embedded network sensor systems
Detecting Violent Scenes in Movies by Auditory and Visual Cues
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Behavior-Sensitive User Interfaces for Smart Environments
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Hydra: a hybrid recommender system [cross-linked rating and content information]
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series
Fuzzy Sets and Systems
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
Pattern recognition and classification for multivariate time series
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Audio-Visual fusion for detecting violent scenes in videos
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Link prediction on evolving data using tensor factorization
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
PIKM 2011: the 4th ACM workshop for Ph.D. students in information and knowledge management
Proceedings of the 20th ACM international conference on Information and knowledge management
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Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the data mining community is the recognition of recurring patterns within temporal databases or streaming data. This dissertation proposal aims at developing and investigating efficient methods for the recognition of contextual patterns in multivariate time series in different application domains based on machine learning techniques. To this end, we propose a generic three-step approach that involves (1) feature extraction to build robust learning models based on significant time series characteristics, (2) segmentation to identify internally homogeneous time intervals and change points, as well as (3) clustering and/or classification to group the time series (segments) into the sub-population to which they belong to. To support our proposed approach, we present and discuss first experiments on real-life vehicular data. Furthermore we describe a number of applications, where pattern recognition in multivariate time series is practical or rather necessary.