Algorithms for clustering data
Algorithms for clustering data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast hierarchical clustering and other applications of dynamic closest pairs
Journal of Experimental Algorithmics (JEA)
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
EigenGait: Motion-Based Recognition of People Using Image Self-Similarity
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
The First Subquadratic Algorithm for Complete Linkage Clustering
ISAAC '95 Proceedings of the 6th International Symposium on Algorithms and Computation
Feature-based classification of time-series data
Information processing and technology
Learning to Recognize Time Series: Combining ARMA models with memory-based learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Automatic clustering of vector time-series for manufacturing machine monitoring
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Optimizing Similarity Search for Arbitrary Length Time Series Queries
IEEE Transactions on Knowledge and Data Engineering
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
KDD-2006 workshop report: Theory and Practice of Temporal Data Mining
ACM SIGKDD Explorations Newsletter
Sensor-Based Abnormal Human-Activity Detection
IEEE Transactions on Knowledge and Data Engineering
Structure-Based Statistical Features and Multivariate Time Series Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Video Mining
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Multivariate timeseries become a popular data form to represent images, that are used as suitable inputs to higher-level recognition processes. We present a novel cluster analysis based on timeseries structure to identify similar human motion sequences. To clustering sequences, the movement silhouettes from video were transformed into low-dimensional multivariate timeseries, then further converted into vectors based on their structure in a finite-dimensional Euclidean space. The identification and selection of structural metrics for human motion sequences were highlighted to demonstrate that these statistical features are generic but also problem dependent. Various clustering algorithms were used to demonstrate the effectiveness and simplicity using real data sets.