@thesis{thesis,
		author={Dian Palupi  and Novi  and TRI KURNIA },
		title ={PENGARUH PERHITUNGAN RENTANG DATA TERHADAP PERFORMANSI ALGORITMA CLUSTERING SELF ORGANIZING MAP},
		year={2018},
		url={https://repository.unsri.ac.id/9955/},
		abstract={Clustering is a valuable research field in data mining. Clustering groups data based on its proximity or similarity. An important component in clustering is the distance measurement between data points. The difference in distance measurement method of data will affect how close the distance between the data. Therefore, this research will examine the effect of distance measurement of data on the performance of clustering algorithm. This study uses Self Organizing Map algorithm. There are three distance measurement method are Euclidean distance, Manhattan distance, and Chebyshev distance. This research uses 4 data set are Iris, Wine, Car Evaluation, and Abalone. The cluster will be evaluated using Davies Bouldin Index. Self Organizing Map and Chebyshev distance produce better cluster than Euclidean distance and Manhattan distance. However, the computation time is longer than Euclidean distance and Manhattan distance.}
		}