Business Intelligence

A Conceptual Approach to Temporal Weighted Item Set Utility Mining

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Executive Summary

Conventional Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. Until recently, rarity has not received much attention in the context of data mining. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. Most Association Rule Mining (ARM) algorithms concentrate on mining frequent itemsets from crisp data and recently, use of discrete utility values. Unfortunately, in most real-life applications, use of discrete valued utilities alone is inadequate. In many cases where these values are uncertain, a fuzzy representation may be more appropriate.

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