MALM: a framework for mining sequence database at multiple abstraction levels
Proceedings of the seventh international conference on Information and knowledge management
Classification Rules + Time = Temporal Rules
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
Distribution Discovery: Local Analysis of Temporal Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subsequence matching on structured time series data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Making Subsequence Time Series Clustering Meaningful
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling and time warping in time series querying
The VLDB Journal — The International Journal on Very Large Data Bases
Classification of multivariate time series using two-dimensional singular value decomposition
Knowledge-Based Systems
Classification of multivariate time series using locality preserving projections
Knowledge-Based Systems
Discovering original motifs with different lengths from time series
Knowledge-Based Systems
Pattern-based time-series subsequence clustering using radial distribution functions
Knowledge and Information Systems
Analysis of Subsequence Time-Series Clustering Based on Moving Average
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel two-level clustering method for time series data analysis
Expert Systems with Applications: An International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
Why does subsequence time-series clustering produce sine waves?
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Shape-based template matching for time series data
Knowledge-Based Systems
A clustering technique for news articles using WordNet
Knowledge-Based Systems
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Subsequence Time Series (STS) Clustering is a time series mining task used to discover clusters of interesting subsequences in time series data. Many research works had used this algorithm as a subroutine in rule discovery, indexing, classification and anomaly detection. Unfortunately, recent work has demonstrated that almost all of the STS clustering algorithms give meaningless results, as their outputs are always produced in sine wave form, and do not associate with actual patterns of the input data. Consequently, algorithms that use the results from the STS clustering as their input will fail to produce its meaningful output. In this work, we propose a new STS clustering framework for time series data called Selective Subsequence Time Series (SSTS) clustering which provides meaningful results by using an idea of data encoding to cluster only essential subsequences. Furthermore, our algorithm also automatically determines an appropriate number of clusters without user's intervention.