A New Approach of Self-adaptive Discretization to Enhance the Apriori Quantitative Association Rule Mining

2012 ◽  
Author(s):  
Dancheng Li ◽  
Ming Zhang ◽  
Shuangshuang Zhou ◽  
Chen Zheng
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
New Approach ◽  
Adaptive Discretization ◽  
Quantitative Association Rule ◽  
Self Adaptive

Association Rule and Quantitative Association Rule Mining among Infrequent Items

2010 ◽  
pp. 15-32 ◽  
Author(s):  
Ling Zhou ◽  
Stephen Yau
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Transactional Databases ◽  
Frequent Items ◽  
Increasing Demand ◽  
Quantitative Association Rule

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the authors propose two simple, practical and effective schemes to mine association rules among rare items. Their algorithms can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. The authors’ schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, they explore quantitative association rule mining in transactional databases among infrequent items by associating quantities of items: some interesting examples are drawn to illustrate the significance of such mining.

scholarly journals Machine Learning Based Quantitative Association Rule Mining Method for Evaluating Cellular Network Performance

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166815-166822
Author(s):  
Guanghui Fan ◽  
Wenjuan Shi ◽  
Liang Guo ◽  
Jun Zeng ◽  
Kaixuan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Association Rule ◽  
Association Rule Mining ◽  
Cellular Network ◽  
Network Performance ◽  
Mining Method ◽  
Rule Mining ◽  
Quantitative Association Rule

Image Classification and Retrieval with Mining Technologies

2010 ◽  
pp. 96-110 ◽  
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Classification ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Technology ◽  
New Approach ◽  
Retrieval Error ◽  
Computation Efficiency ◽  
Novel Method ◽  
Two Sides

Mining techniques can play an important role in automatic image classification and content-based retrieval. A novel method for image classification based on feature element through association rule mining is presented in this chapter. The effectiveness of this method comes from two sides. The visual meanings of images can be well captured by discrete feature elements. The associations between the description features and the image contents can be properly discovered with mining technology. Experiments with real images show that the new approach provides not only lower classification and retrieval error but also higher computation efficiency.

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