An Efficient Algorithm for Mining Weighted Frequent Itemsets Using Adaptive Weights
| Autor: | Hung Long Nguyen |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Control and Optimization
Computer Networks and Communications Computer science Efficient algorithm Data stream mining Computation Retail market Improved algorithm InformationSystems_DATABASEMANAGEMENT computer.software_genre Computer Science Applications Human-Computer Interaction Artificial Intelligence Modeling and Simulation Signal Processing Scalability Click stream analysis Data mining computer |
| Zdroj: | International Journal of Intelligent Systems and Applications. 7:41-48 |
| ISSN: | 2074-9058 2074-904X |
| Popis: | Weighted frequent itemset mining is more practical than traditional frequent itemset mining, because it can consider different semantic significance (weight) of items. Many models and algorithms for mining weighted frequent itemsets have been proposed. These models assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of the items may vary with time. Therefore, reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. Recently, Chowdhury F. A. et al. have introduced a novel concept of adaptive weight for each item and propose an algorithm AWFPM (Adaptive Weighted Frequent Pattern Mining). AWFPM can handle the situation where the weight (price or significance) of an item may vary with time. In this paper, we present an improved algorithm named AWFIMiner. Experimental computations show that our AWFIMiner is more efficient and scalable for mining weighted frequent itemsets using adaptive weights. Moreover, because it only requires one single database scan, the AWFIMiner is applicable for mining these itemsets on data streams. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|