Abstract
Erasable Itemset Mining is the key approach of data mining in production planning. The erasable itemset mining is the process of finding erasable itemsets that satisfy the constraint i.e. user defined threshold. Efficient algorithm to mine erasable itemsets is extremely important in data mining. Since the META Algorithm was proposed to generate the erasable itemsets. In last few years there have been several methods to improve its performance. But they do not consider the time constraint. If database is large META takes too much time to scan the database. In this paper, Author purposed an Improved META (I-META) algorithm which reduces the scanning time by reduction of production records. It also reduces the redundant generation of sub-items during trimming the candidate itemsets, which can find directly the set of erasable itemsets and removing candidate having a subset that is not erasable.
Highlights
Data mining is emerging field in database system and new database applications [10]
Frequent pattern mining concept worked in many fields such as association rule mining, pattern based classification, clustering, finding correlated item, erasable itemset mining etc [12]
Author has discussed some research papers which had been previously undertaken in the field of frequent pattern mining and erasable itemsets mining
Summary
Data mining is emerging field in database system and new database applications [10]. It is the process of finding useful information in large database and finding new relationship among datasets. Frequent pattern mining (FPM) is the important data mining concept which discovers frequent itemset in database [1]. Frequent patterns are the patterns that appear in database frequently. Frequent pattern mining concept worked in many fields such as association rule mining, pattern based classification, clustering, finding correlated item, erasable itemset mining etc [12]
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