Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient and robust feature extraction and pattern matching of time series by a lattice structure
Proceedings of the tenth international conference on Information and knowledge management
Cluster Analysis of Biomedical Image Time-Series
International Journal of Computer Vision
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Multivariate Clustering by Dynamics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Fuzzy C-Means Clustering-Based Speaker Verification
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Clustering seasonality patterns in the presence of errors
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Exact indexing of dynamic time warping
Knowledge and Information Systems
Clustering Time Series with Clipped Data
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Data Mining and Knowledge Discovery
Online clustering of parallel data streams
Data & Knowledge Engineering
An Interweaved HMM/DTW Approach to Robust Time Series Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Clustering Time Series with Granular Dynamic Time Warping Method
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Clustering Distributed Time Series in Sensor Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Clustering of time series data-a survey
Pattern Recognition
Similarity-based clustering of sequences using hidden Markov models
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A likelihood ratio distance measure for the similarity between the fourier transform of time series
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
| Hi-index | 12.05 |
Time series is a very popular type of data which exists in many domains. Clustering time series data has a wide range of applications and has attracted researchers from a wide range of discipline. In this paper a novel algorithm for shape based time series clustering is proposed. It can reduce the size of data, improve the efficiency and not reduce the effects by using the principle of complex network. Firstly, one-nearest neighbor network is built based on the similarity of time series objects. In this step, triangle distance is used to measure the similarity. Of the neighbor network each node represents one time series object and each link denotes neighbor relationship between nodes. Secondly, the nodes with high degrees are chosen and used to cluster. In clustering process, dynamic time warping distance function and hierarchical clustering algorithm are applied. Thirdly, some experiments are executed on synthetic and real data. The results show that the proposed algorithm has good performance on efficiency and effectiveness.