Constraint propagation algorithms for temporal reasoning: a revised report
Readings in qualitative reasoning about physical systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery from Series of Interval Events
Journal of Intelligent Information Systems - Data warehousing and knowledge discovery
Maintaining knowledge about temporal intervals
Communications of the ACM
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Adding Temporal Semantics to Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Discovery of Sequential Patterns by Memory Indexing
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Fluent Learning: Elucidating the Structure of Episodes
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
On Computing Condensed Frequent Pattern Bases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Generating English summaries of time series data using the Gricean maxims
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Amnesic Approximation of Streaming Time Series
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Approximating a collection of frequent sets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Visually mining and monitoring massive time series
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Discovering interpretable muscle activation patterns with the temporal data mining method
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Optimizing time series discretization for knowledge discovery
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Learning models and formulas of a temporal event logic
Learning models and formulas of a temporal event logic
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficiently Mining Frequent Closed Partial Orders
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A systematic approach to the assessment of fuzzy association rules
Data Mining and Knowledge Discovery
Discovering Frequent Closed Partial Orders from Strings
IEEE Transactions on Knowledge and Data Engineering
\delta-Tolerance Closed Frequent Itemsets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Extracting interpretable muscle activation patterns with time series knowledge mining
International Journal of Knowledge-based and Intelligent Engineering Systems
Data & Knowledge Engineering
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
An efficient algorithm for mining time interval-based patterns in large database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Significant motifs in time series
Statistical Analysis and Data Mining
Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Case-based reasoning in comparative effectiveness research
IBM Journal of Research and Development
Discovering metric temporal constraint networks on temporal databases
Artificial Intelligence in Medicine
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We present a new method for the understandable description of local temporal relationships in multivariate data, called Time Series Knowledge Mining (TSKM). We define the Time Series Knowledge Representation (TSKR) as a new language for expressing temporal knowledge in time interval data. The patterns have a hierarchical structure, with levels corresponding to the temporal concepts duration, coincidence, and partial order. The patterns are very compact, but offer details for each element on demand. In comparison with related approaches, the TSKR is shown to have advantages in robustness, expressivity, and comprehensibility. The search for coincidence and partial order in interval data can be formulated as instances of the well known frequent itemset problem. Efficient algorithms for the discovery of the patterns are adapted accordingly. A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms. Human interaction is used during the mining to analyze and validate partial results as early as possible and guide further processing steps. The efficacy of the methods is demonstrated using two real life data sets. In an application to sports medicine the results were recognized as valid and useful by an expert of the field.