ANALISIS KELOMPOK UNTUK DATA KATEGORIK BERDASARKAN ALGROITMA VEKTOR HAMMING DISTANCE (Studi Kasus : Klasifikasi Hewan Berdasarkan Morfologi dan Karakteristik Umum) - , SHIDDIQ SUGIONO ([Yogyakarta] : Universitas Gadjah Mada, 2014)
Clustering analysis using K-Means clustering methods which using mean as the center of cluster doesnât have any meaning anymore if the method is used for the categorical data. Clustering analysis using Hamming distance vector algorithm is only for categorical dataset. Theory that used in this algorithm is based on coding theory. It doesnât need any model or convergence criteria for this algorithm. This algorithm is the alternate way for older method : K-Modes and Autoclass. The case studies in this thesis is animal classification based on their morphology and common characteristic, the purpose of the classification is grouping some animal into subpopulation. Thus, the result can be a reference to studing animal from their simmilarities.