Bayesian Networks and Association Analysis in Knowledge Discovery Process
Derya Ersel, Süleyman Günay · 2014
Data mining is a statistical process to extract useful information, unknown patterns and interesting relationships in large databases. In this process, many statistical methods are used. Two of these methods are Bayesian networks and association analysis. Bayesian networks are probabilistic graphical models that encode relationships among a set of random variables in a database. Since they have both causal and probabilistic aspects, data information and expert knowledge can easily be combined by them. Bayesian networks can also represent knowledge about uncertain domain and make strong inferences. Association analysis is a useful technique to detect hidden associations and rules in large databases, and it extracts previously unknown and surprising patterns from already known information. A drawback of association analysis is that many patterns are generated even if the data set is very small. Hence, suitable interestingness measures must be performed to eliminate uninteresting patterns. Bayesian networks and association analysis can be used together in knowledge discovery. As association rules are used to create Bayesian networks, interestingness measures to determine interesting patterns can be established by Bayesian networks. In this study, this mutual utilization between Bayesian Networks and association analysis is explained and an illustration over a real life