Identifying Calendar-Based Periodic Patterns
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Summary
This paper proposes a hash based data structure for storing and managing patterns for finding calendar-based periodic patterns and provides a detailed data analysis incorporating various parameters of the algorithm and makes a comparative analysis with the existing algorithm.
- Published
- 2013-01-01
- Cited by
- 12
- References
- 22
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:61501573
References
- A Framework for Synthesizing Arbitrary Boolean Expressions Induced by Frequent Itemsets
- Fast Algorithms for Mining Association Rules in Large Databases
- Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, May 25-28, 2005, Proceedings
- Time Granularities in Databases, Data Mining, and Temporal Reasoning
- Cyclic association rules
- An approach to discovering temporal association rules
- Mining Frequent Temporal Patterns in Interval Sequences
- A parallel algorithm for mining multiple partial periodic patterns
- Efficient mining of both positive and negative association rules
- An effective mining approach for up-to-date patterns
- Mining temporal interval relational rules from temporal data
- Finding calendar-based periodic patterns
- Mining conditional patterns in a database
- A Survey of Temporal Knowledge Discovery Paradigms and Methods
- Mining sequential patterns
- Maintaining knowledge about temporal intervals
- Discovering calendar-based temporal association rules
- Mining association rules between sets of items in large databases
- Discovering Partial Periodic Sequential Association Rules with Time Lag in Multiple Sequences for Prediction
- Finding Locally and Periodically Frequent Sets and Periodic Association Rules
Cited by
- Mining Calendar-based Periodic Patterns from Nonbinary Transactions
- A State-of-the-Art Review of Knowledge Discovery in Multiple Databases
- A New Methodology for Mining Frequent Itemsets on Temporal Data
- Fast Uplink Grant for Machine Type Communications: Challenges and Opportunities
- A Systematic Literature Review of Frequent Pattern Mining Techniques
- A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction
- Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications
- Mining Periodic Patterns from Non-binary Transactions
- Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
- Mining Frequent Itemset on Temporal Data using Hybrid Algorithm
- Mining Calendar-Based Periodic Patterns in Time-Stamped Data
- Mining Frequent Itemset on Temporal Data by Using Improved Apriori Algorithm
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