Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Programmable Stream Processors
Computer
Research issues in data stream association rule mining
ACM SIGMOD Record
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Finding temporal patterns in noisy longitudinal data: a study in diabetic retinopathy
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Trend mining in social networks: a study using a large cattle movement database
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
Time series visualization based on shape features
Knowledge-Based Systems
Time series symbolization and search for frequent patterns
Proceedings of the Fourth Symposium on Information and Communication Technology
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In this paper we present the dual support Apriori for temporal data (DSAT) algorithm. This is a novel technique for discovering jumping and emerging patterns (JEPs) from time series data using a sliding window technique. Our approach is particularly effective when performing trend analysis in order to explore the itemset variations over time. Our proposed framework is different from the previous work on JEP in that we do not rely on itemsets borders with a constrained search space. DSAT exploits previously mined time stamped data by using a sliding window concept, thus requiring less memory, minimum computational cost and very low dataset accesses. DSAT discovers all JEPs, as in ''naive'' approaches, but utilises less memory and scales linearly with large datasets sets as demonstrated in the experimental section.