Future direction of incremental association rules mining
Ahmed Emam
Abstract
Ahmed Emam
Abstract
Data mining has been attracted much attention from practitioners and researchers in recent years. Association rules are one of the most important research areas of data mining. Association Rule Mining (ARM) aims to discovers the relationship between the most frequent itemsets. Many algorithms have been developed for mining static datasets. It is nontrivial to maintain such discovered rules from large datasets, this was the main idea behind Incremental Association Rules Mining (IARM), which recently has received much attention from the Data Mining researcher. In this paper, a survey of Incremental Association Rule Mining (IARM) techniques and algorithms that had been developed are categorized and analyzed.
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Data mining has been attracted much attention from practitioners and researchers in recent years. Association rules are one of the most important research areas of data mining. Association Rule Mining (ARM) aims to discovers the relationship between the most frequent itemsets. Many algorithms have been developed for mining static datasets. It is nontrivial to maintain such discovered rules from large datasets, this was the main idea behind Incremental Association Rules Mining (IARM), which recently has received much attention from the Data Mining researcher. In this paper, a survey of Incremental Association Rule Mining (IARM) techniques and algorithms that had been developed are categorized and analyzed.
Key concepts: Association rule learning, Data mining, Computer science, Association (psychology), K-optimal pattern discovery, Data science, Epistemology, Philosophy