Combination of K-Means method with Davies Bouldin index and decision tree method with parameter optimization for best performance.

The K-Means method is the most popular and often used function in Data Mining in data grouping. One of the shortcomings of the K-Means method is the determination of the number of clusters that have not been optimal. This study will develop a combination of grouping datasets using the K-Means Clustering Method with the Davies Bouldin Index (DBI) for determining the most optimal number of clusters. The result of clustering is then classified using the Decision Tree Method with parameter optimization to produce the best performance indicated by the highest accuracy, recall and precision values. The processed dataset is public data from UCI Machine Learning Repository which is divided into training data and testing data. From performance tests conducted for the number of clusters 3 to 10, it is known that the most optimal number of clusters is 3 with the smallest Index Davies Bouldin value of 0.626 where cluster 0 has 490 members, cluster 1 has 124 members and cluster 2 has 197 members. Testing classification methods with K-Cross Validation for training data resulted in an accuracy value of 98.06 with a precision value of 97.64 and a recall value of 97.49. For data testing generate accuracy value 97.12 with precision value 97.10 and recall value 96.05. Because the accuracy value of>90, the classification in this study is excellent clasification. [ABSTRACT FROM AUTHOR]