A New Method for Image Classification by Using Multilevel Association Rules

2005 ◽  
Author(s):  
V.S. Tseng ◽  
Ming-Hsiang Wang ◽  
Ja-Hwung Su
Keyword(s):  
Image Classification ◽  
Association Rules ◽  
New Method ◽  
Multilevel Association Rules

A Novel Efficient Mining Association Rules Algorithm for Distributed Databases

2010 ◽  
Vol 108-111 ◽  
pp. 50-56 ◽  
Author(s):  
Liang Zhong Shen
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
New Method ◽  
Rule Mining ◽  
Mining Association Rules

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate professionals, association rule mining is receiving increasing attention. The technology of data mining is applied in analyzing data in databases. This paper puts forward a new method which is suit to design the distributed databases.

E-MsNFIS: Efficient Negative Frequent Itemsets Mining Based on Multiple Minimum Supports

2013 ◽  
Vol 411-414 ◽  
pp. 386-389 ◽  
Author(s):  
Tian Tian Xu ◽  
Xiang Jun Dong
Keyword(s):  
Association Rules ◽  
Real Life ◽  
Frequent Itemsets ◽  
Negative Association ◽  
Experimental Results ◽  
New Method ◽  
Minimum Support ◽  
Frequent Itemsets Mining ◽  
Negative Association Rules ◽  
Multiple Minimum Supports

Negative frequent itemsets (NFIS) like (a1a2¬a3a4) have played important roles in real applications because we can mine valued negative association rules from them. In one of our previous work, we proposed a method, namede-NFISto mine NFIS from positive frequent itemsets (PFIS). However,e-NFISonly uses single minimum support, which implicitly assumes that all items in the database are of the same nature or of similar frequencies in the database. This is often not the case in real-life applications. So a lot of methods to mine frequent itemsets with multiple minimum supports have been proposed. These methods allow users to assign different minimum supports to different items. But these methods only mine PFIS, doesn’t consider negative ones. So in this paper, we propose a new method, namede-msNFIS, to mine NFIS from PFIS based on multiple minimum supports. E-msNFIScontains three steps: 1) using existing methods to mine PFIS with multiple minimum supports; 2) using the same method ine-NFISto generate NCIS from PFIS got in step 1; 3) calculating the support of these NCIS only using the support of PFIS and then gettingNFIS. Experimental results show that thee-msNFISis efficient.

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