Comparison of distance metric in k-mean algorithm for clustering wheat grain datasheet

Abstract

One of the data mining models is clustering, clustering models can be used to create groupings of data. Clustering is done by creating groups of data that are close to each other. The research was conducted by clustering wheat seed datasheets.  The wheat grain datasheet contains various types of wheat data.  The purpose of this research is to create a clustering model. The algorithm used is the K-means algorithm and a comparison is made with several distance Metric algorithms. The datasheet used was tested with the K-means algorithm and tested the clustering value (k) ranging from k = 2 to k = 6. Comparison of clustering results with K-means is also done by comparing with distance metric algorithms, namely Euclidean distance, Manhattan distance, and Chebychev distance.  All testing processes are evaluated, and the evaluation is done to select many good groupings. The evaluation process is carried out using the Davis-Bouldin method. The results of the grouping that has been done, each seen Davis Bouldin evaluation. The evaluation value of Davis Bouldin is sought from the smallest value and if the evaluation result is negative, the value is solved. The research method used is Knowledge Discovery in Database (KDD). The results showed that the same datasheet and using the K-means algorithm and the same evaluation resulted in different evaluation values. The Euclidian, Manhattan, and Chebychev algorithms produce the best k value of 2, The conclusion of the wheat seed datasheet clustering research produces a value of k = 2