Mining Frequent Patterns on Dynamic Weighted Databases
Main Article Content
Abstract
Pattern mining is a fundamental task in modern data mining. Among its various branches, mining frequent weighted itemsets (FWIs) from quantitative databases stands as a significant problem, aiming to discover prevalent patterns within such datasets. However, current research has not adequately addressed quantitative databases where item weights dynamically change over time, known as dynamic weighted databases (dWDB). In real-world scenarios, the importance of items, such as product profitability or relevance at specific times, often fluctuates (e.g., air conditioners sell more in summer, and medical masks gain significance during respiratory disease outbreaks). This paper introduces a novel problem: mining FWIs with dynamic weighted items from quantitative databases, termed dynamic quantitative databases. We then propose dFWIT, an algorithm that leverages the traditional tidset structure to tackle this problem. Furthermore, we develop dFWNL, another algorithm that utilizes a new data structure called dWNList for mining FWIs from dWDB. Finally, we conduct experiments on various dWDBs to demonstrate the efficacy of the proposed algorithms
Keywords
Weighted database Mẫu phổ biến: Frequent pattern CSDL trọng số động: Dynamic weighted database Cấu trúc WNlist: WNList structure
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References
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