• Journal of the Korean Data and Information Science Society
• /
• v.22 no.1
• /
• pp.81-88
• /
• 2011

Association rule mining searches for interesting relationships among items in a given database. There are three primary quality measures for association rule, support and confidence and lift. In order to improve the efficiency of existing mining algorithms, constraints were applied during the mining process to generate only those association rules that are interesting to users instead of all the association rules. When we create relation rule, we can often find a lot of rules. This can find rule that direct relativity by intervening variable does not exist. In this study we try to discovery an insignificant rule in association rules by intervening variable. Result of this study can understand relativity about rule that is created in relation rule more exactly.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.1
• /
• pp.121-129
• /
• 2012

Association rule mining searches for interesting relationships among items in a given database. Currently, study of the constraint-based association rules are underway by many researchers. When we create relation rule, we can often find a lot of rules. Of this rules, we can find rule that direct relativity by marginally conditional variables (intervening variable, external variable) does not exist. In such a case, this association rule can be considered insignificant. In this study, we want to study for association rules creation using marginally conditional variable. The result of this study can find meaningless association rules. Also, we can understand more exactly the relationships between variables.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.671-678
• /
• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.5
• /
• pp.983-992
• /
• 2012

Social indicator survey can identify the state of society as a whole. When we create a policy, social indicator survey can reflect the public opinion of the region. Social indicator survey is an important measure of social change. Social indicator survey has been conducted in many municipalities (Seoul, Incheon, Busan, Ulsan, Gyeongsangnamdo, etc.). But, the result of social indicator survey analysis is mainly the basic statistical analysis. In this study, we propose a new data mining methodology for effective analysis. We propose a 3-step complex data mining in society indicator survey. 3-step complex data mining uses three data mining method (intervening association rule, clustering, decision tree).