Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Fast Online Dynamic Association Rule Mining
WISE '01 Proceedings of the Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1 - Volume 1
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Matrix apriori: speeding up the search for frequent patterns
DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
Top 10 algorithms in data mining
Knowledge and Information Systems
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
Incremental Mining with Prelarge Trees
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Mining Frequent Itemsets in a Stream
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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Databases are updated continuously with increments and re-running the frequent itemset mining algorithms with every update is inefficient. Studies addressing incremental update problem generally propose incremental itemset mining methods based on Apriori and FP-Growth algorithms. Besides inheriting the disadvantages of base algorithms, incremental itemset mining has challenges such as handling i) increments without re-running the algorithm, ii) support changes, iii) new items and iv) addition/deletions in increments. In this paper, we focus on the solution of incremental update problem by proposing the Incremental Matrix Apriori Algorithm. It scans only new transactions, allows the change of minimum support and handles new items in the increments. The base algorithm Matrix Apriori works without candidate generation, scans database only twice and brings additional advantages. Performance studies show that Incremental Matrix Apriori provides speed-up between 41% and 92% while increment size is varied between 5% and 100%.