Why does subsequence time-series clustering produce sine waves?

Authors:
Tsuyoshi Idé
Affiliations:
Tokyo Research Laboratory, IBM Research, Kanagawa, Japan
Venue:
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Year:
2006

Citing 6
Cited 7

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
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Kernel k-means: spectral clustering and normalized cuts

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
K-means clustering via principal component analysis

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Making Subsequence Time Series Clustering Meaningful

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Pairwise Symmetry Decomposition Method for Generalized Covariance Analysis

ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining

In search of meaning for time series subsequence clustering: matching algorithms based on a new distance measure

CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation

IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Data mining of vector–item patterns using neighborhood histograms

Knowledge and Information Systems
Translational symmetry in subsequence time-series clustering

JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction

Data Mining and Knowledge Discovery
Traffic events modeling for structural health monitoring

IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Short communication: Selective Subsequence Time Series clustering

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The data mining and machine learning communities were surprised when Keogh et al. (2003) pointed out that the k-means cluster centers in subsequence time-series clustering become sinusoidal pseudo-patterns for almost all kinds of input time-series data. Understanding this mechanism is an important open problem in data mining. Our new theoretical approach (based on spectral clustering and translational symmetry) explains why the cluster centers of k-means naturally tend to form sinusoidal patterns.