Frequent Itemset Mining: A Metadata Based Approach for Knowledge Discovery
DOI:
https://doi.org/10.26438/ijcse/v6i3.316320Keywords:
Web Multimedia Mining, Association rule, Frequent itemsets, Knowledge discoveryAbstract
Frequent sets play a crucial role in many Data Mining tasks that try to find interesting patterns from databases, such as correlations, association rules, classification and clustering. The Association Rules is one of the most used functions in data mining. The method is used both database researchers and data mining users. In this article, association rule mining algorithms are discussed and demonstrated. Mining Associate rule algorithm that search for approximate strong association rules from multimedia databases. The Apriori-like sequential pattern mining approach based on candidate generates-and test can also be explored by mapping a sequence multimedia database into vertical data format. This approach is useful to finding frequent itemsets, which probabilistic frequent itemsets based on possible datasets.
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