An Efficient High Utility Frequent Itemsets Mining Using Fast Apriori Based Hierarchical Clustering Algorithm

Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Mining high utility itemsets from databases is an important data mining task for discovery of itemsets with high utilities. However, it may present too many HUIs to users, which also degrades the efficiency of the mining process. Frequent Item set Mining (FIM) is a one of its popular applications is market basket analysis, which refers to the discovery of sets of items (itemsets) that are frequently purchased together by customers. In this paper, presents a new system to utilize the model for building a Lossless Representation system that suggests high utility itemsets over dynamic datasets using the Fast Apriori closed High Utility item set discovery with hierarchical clustering algorithm (FAHU-Hierarchical).

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