scholarly journals Mining Association Rule for the Abnormal Event in Data Stream Systems

2007 ◽  
Vol 14D (5) ◽  
pp. 483-490 ◽  
Author(s):  
Dae-In Kim ◽  
Joon Park ◽  
Bu-Hyun Hwang
Keyword(s):  
Association Rule ◽  
Data Stream ◽  
Mining Association Rule ◽  
Stream Systems

scholarly journals Mining association rule based on the diseases population for recommendation of medicine need

2018 ◽  
Vol 1007 ◽  
pp. 012017 ◽  
Author(s):  
M Harahap ◽  
A M Husein ◽  
S Aisyah ◽  
F R Lubis ◽  
B A Wijaya
Keyword(s):  
Association Rule ◽  
Rule Based ◽  
Mining Association Rule

scholarly journals FAST ALGORITHM FOR MINING ASSOCIATION RULE

2006 ◽  
Vol 34 (1) ◽  
pp. 79-87
Author(s):  
M. H. Margahny ◽  
A. Shakour
Keyword(s):  
Fast Algorithm ◽  
Association Rule ◽  
Mining Association Rule

Dynamic Plan Migration for Snapshot-Equivalent Continuous Queries in Data Stream Systems

2006 ◽  
pp. 497-516 ◽  
Author(s):  
Jürgen Krämer ◽  
Yin Yang ◽  
Michael Cammert ◽  
Bernhard Seeger ◽  
Dimitris Papadias
Keyword(s):  
Data Stream ◽  
Continuous Queries ◽  
Stream Systems

Visual Data Mining for Discovering Association Rules

2008 ◽  
pp. 2105-2120
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Visual Data Mining for Discovering Association Rules

2011 ◽  
pp. 209-226
Author(s):  
Kesaraporn Techapichetvanich ◽  
Amitava Datta
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Data Sets ◽  
Visual Data Mining ◽  
Useful Knowledge ◽  
Large Databases ◽  
A New Technique ◽  
Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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