Entropy-based associative classification algorithm for mining manufacturing data

This paper presents a new associative classification algorithm for data mining. The algorithm uses elementary set concepts, information entropy and database manipulation techniques to develop useful relationships between input and output attributes of large databases. These relationships (knowledge) are represented using IF-THEN association rules, where the IF portion of the rule includes a set of input attributes features and THEN portion of the rule includes a set of output attributes that represent decision outcome. Application of the algorithm is presented with a thermal spray process control case study. Thermal spray is a process of forming a desired shape of material by spraying melted metal on a ceramic mould. The goal of the study is to identify spray process input parameters that can be used to effectively control the process with the purpose of obtaining better characteristics for the sprayed material. Detailed discussion on the source and characteristics of the data sets is also presented.