Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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When the time dimension is added to datasets, time series data are obtained. Extracting knowledge from time series data requires special attention to the timing aspects of the data. An interesting activity in the field of knowledge discovery from time series data is predicting the timing of upcoming events. In this paper we present a method for mining fuzzy knowledge from time series data. In contrast to traditional time series analysis methods which largely focus on global models, our method is about the discovery of local patterns in time series. The extracted knowledge will be in the form of fuzzy association rules and it aims at predicting the approximate timing of upcoming events. The proposed method includes cleaning and filtering of time series data, segmenting time series, extracting important features for prediction, further cleaning on feature values, fuzzifying feature values, extracting fuzzy association rules, and pruning the discovered rules. We will show the efficiency of our approach on a stock market dataset.