K-Nearest Neighbor classification for glass identification problem

The discovery of knowledge form criminal evidence databases is important in order to make effective criminological investigation. The aim of data mining is to extract knowledge from database and produce unambiguous and reasonable patterns. K-Nearest Neighbor (KNN) is one of the most successful data mining methods used in classification problems. Many researchers show that combining different classifiers through voting resulted in better performance than using single classifiers. This paper applies KNN to help criminological investigators in identifying the glass type. It also checks if integrating KNN with another classifier using voting can enhance its accuracy in indentifying the glass type. The results show that applying voting can enhance the KNN accuracy in the glass identification problem.