9 citations · 16 across the 8 of their papers we have counts for
8 papers
Mining top-k granular association rules for recommendation
Fan Min, William Zhu
Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, e…
Cold-start recommendation through granular association rules
Fan Min, William Zhu
Recommender systems are popular in e-commerce as they suggest items of interest to users. Researchers have addressed the cold-start problem where either the user or the item is new…
Granular association rules for multi-valued data
Fan Min, William Zhu
Granular association rule is a new approach to reveal patterns hide in many-to-many relationships of relational databases. Different types of data such as nominal, numeric and mult…
Cost-Sensitive Feature Selection of Data with Errors
Hong Zhao, Fan Min, William Zhu
In data mining applications, feature selection is an essential process since it reduces a model's complexity. The cost of obtaining the feature values must be taken into considerat…
Minimal cost feature selection of data with normal distribution measurement errors
Hong Zhao, Fan Min, William Zhu
Minimal cost feature selection is devoted to obtain a trade-off between test costs and misclassification costs. This issue has been addressed recently on nominal data. In this pape…
Test-cost-sensitive attribute reduction of data with normal distribution measurement errors
Hong Zhao, Fan Min, William Zhu
The measurement error with normal distribution is universal in applications. Generally, smaller measurement error requires better instrument and higher test cost. In decision makin…