An efficient sequential clustering method
Pattern Recognition
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
Large Vocabulary Recognition of On-Line Handwritten Cursive Words
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Scalable Algorithm for Clustering Sequential Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Unsupervised Analysis of Human Gestures
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Trajectory Segmentation Using Dynamic Programming
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compression of motion capture databases
ACM SIGGRAPH 2006 Papers
Global distance-based segmentation of trajectories
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Online clustering of parallel data streams
Data & Knowledge Engineering
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
Robust Time-Referenced Segmentation of Moving Object Trajectories
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Robust Time-Referenced Segmentation of Moving Object Trajectories
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
An algorithmic framework for segmenting trajectories based on spatio-temporal criteria
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
An agent-based approach to care in independent living
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
SeTraStream: semantic-aware trajectory construction over streaming movement data
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Revisiting the k-means algorithm for fast trajectory segmentation
ACM SIGGRAPH 2011 Posters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The ClusTree: indexing micro-clusters for anytime stream mining
Knowledge and Information Systems
StreamKM++: A clustering algorithm for data streams
Journal of Experimental Algorithmics (JEA)
Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
The Journal of Machine Learning Research
Actom sequence models for efficient action detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Competitive learning algorithms for robust vector quantization
IEEE Transactions on Signal Processing
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Least squares quantization in PCM
IEEE Transactions on Information Theory
Segmentation and Sampling of Moving Object Trajectories Based on Representativeness
IEEE Transactions on Knowledge and Data Engineering
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
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Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the well-known K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the sum of squared error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of-the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.