Breaking the barrier of transactions: mining inter-transaction association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Principles of data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Data Mining in Time Series Database
Data Mining in Time Series Database
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data & Knowledge Engineering
Efficient mining of frequent episodes from complex sequences
Information Systems
Fuzzy expert systems and challenge of new product pricing
Computers and Industrial Engineering
Marking the Close analysis in Thai Bond Market Surveillance using association rules
Expert Systems with Applications: An International Journal
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This research centres on an experience which deals with multiple financial time-series. Financial data are treated by temporal mining analysis, specifically temporal association rules. These data are captured from records of the Madrid Stock Exchange (IGBM). The main goal focuses on seeking useful knowledge to explain relationships and trends amongst assets/stock prices which determine IGBM stock index. An exploratory methodology based on Knowledge Discovery (KDD) is used to cover all stages of the mining analysis: data extraction, filtering, basic forms representation, finding of important and characteristic episodes/events, construction of temporal-transactional databases and finally, searching and presentation of temporal association rules with their technical and financial conclusions. This methodology is supported on software tools by using developed libraries and graphics in R-free statistical language platform. The basic idea of mining temporal rules consists in searching and representing the repeated relationships between events obtained from these financial time-series by adding time-parameter constraints: time window (sliding window) and time lags. This process involves finding significant events into multivariate time series considering time restrictions, and then a search is made for sequences of episodes or items that are repeated amongst financial data in order to carry out a rules extraction stage. Database is connected to 20 assets (stock price time-series, in Euros) of IGBM and IGBM (index evolution, expressed in points) which conform a set of 21 financial time-series from 1993 to 2008.