Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
MALM: a framework for mining sequence database at multiple abstraction levels
Proceedings of the seventh international conference on Information and knowledge management
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A new approach to analyzing gene expression time series data
Proceedings of the sixth annual international conference on Computational biology
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery
Journal of Intelligent Information Systems
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Classification Rules + Time = Temporal Rules
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combining the Self-Organizing Map and K-Means Clustering for On-Line Classification of Sensor Data
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Indexing and Mining of the Local Patterns in Sequence Database
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Distribution Discovery: Local Analysis of Temporal Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Motion Recognition Method by Using Primitive Motions
VDB 5 Proceedings of the Fifth Working Conference on Visual Database Systems: Advances in Visual Information Management
Extraction of Primitive Motion and Discovery of Association Rules from Human Motion Data
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Data snooping, dredging and fishing: the dark side of data mining a SIGKDD99 panel report
ACM SIGKDD Explorations Newsletter
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Cost-efficient mining techniques for data streams
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGMOD Record
Making Subsequence Time Series Clustering Meaningful
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Unfolding preprocessing for meaningful time series clustering
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Compression-based data mining of sequential data
Data Mining and Knowledge Discovery
Making clustering in delay-vector space meaningful
Knowledge and Information Systems
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Useful clustering outcomes from meaningful time series clustering
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Discovering original motifs with different lengths from time series
Knowledge-Based Systems
Clustering Streaming Time Series Using CBC
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
An efficient stream mining technique
WSEAS Transactions on Information Science and Applications
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
An efficient time series data mining technique
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
On-line motif detection in time series with SwiftMotif
Pattern Recognition
Subspace sums for extracting non-random data from massive noise
Knowledge and Information Systems
Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
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
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
HE-Tree: a framework for detecting changes in clustering structure for categorical data streams
The VLDB Journal — The International Journal on Very Large Data Bases
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
A tree-construction search approach for multivariate time series motifs discovery
Pattern Recognition Letters
A novel two-level clustering method for time series data analysis
Expert Systems with Applications: An International Journal
Point-distribution algorithm for mining vector-item patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
A review on time series data mining
Engineering Applications of Artificial Intelligence
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Data Mining and Knowledge Discovery
Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications
Pattern recognition in multivariate time series: dissertation proposal
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
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
Dimension reduction for clustering time series using global characteristics
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Recent advances in mining time series data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A fast compression-based similarity measure with applications to content-based image retrieval
Journal of Visual Communication and Image Representation
A novel mining algorithm for periodic clustering sequential patterns
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Recent advances in mining time series data
ECML'05 Proceedings of the 16th European conference on Machine Learning
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Clustering time-series medical databases based on the improved multiscale matching
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Locating motifs in time-series data
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Accurate symbolization of time series
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Deductive and inductive reasoning on spatio-temporal data
INAP'04/WLP'04 Proceedings of the 15th international conference on Applications of Declarative Programming and Knowledge Management, and 18th international conference on Workshop on Logic Programming
Clustering distributed data streams in peer-to-peer environments
Information Sciences: an International Journal
ACM Computing Surveys (CSUR)
Modeling topic trends on the social web using temporal signatures
Proceedings of the twelfth international workshop on Web information and data management
Real time processing of data from patient biodevices
HIKM '11 Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management - Volume 120
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Time series data is perhaps the most frequently encountered typeof data examined by the data mining community. Clustering isperhaps the most frequently used data mining algorithm, beinguseful in it's own right as an exploratory technique, and also as asubroutine in more complex data mining algorithms such as rulediscovery, indexing, summarization, anomaly detection, andclassification. Given these two facts, it is hardly surprising thattime series clustering has attracted much attention. The data to beclustered can be in one of two formats: many individual timeseries, or a single time series, from which individual time seriesare extracted with a sliding window. Given the recent explosion ofinterest in streaming data and online algorithms, the latter casehas received much attention.In this work we make an amazing claim. Clustering of streamingtime series is completely meaningless. More concretely, clustersextracted from streaming time series are forced to obey a certainconstraint that is pathologically unlikely to be satisfied by anydataset, and because of this, the clusters extracted by anyclustering algorithm are essentially random. While this constraintcan be intuitively demonstrated with a simple illustration and issimple to prove, it has never appeared in the literature.We can justify calling our claim surprising, since it invalidatesthe contribution of dozens of previously published papers. We willjustify our claim with a theorem, illustrative examples, and acomprehensive set of experiments on reimplementations ofprevious work.