<em><span style="font-size:10pt;" lang="en-us" xml:lang="en-us">Based on the six indicators provided by the State Ministry for </span><span class="pathway"><span style="font-size:10pt;" lang="en-us" xml:lang="en-us">Acceleration Development Backward Regions, </span></span><span style="font-size:10pt;" lang="en-us" xml:lang="en-us"><span> </span>the backward regions were clustered into 4 groups: </span><span style="font-size:10pt;" lang="en-us" xml:lang="en-us">fairly backward region, backward region, highly backward region, and severely backward region<span>. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. </span>The objectives of this research are to study the non-hierarchy cluster methods, that is C-Means and Fuzzy C-Means<span>. Both methods have difference on membership value and weighted membership value. The result of this research showed that Fuzzy C-Means was more robust than C-Means.</span></span></em>

Abstract

Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions,  the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research are to study the non-hierarchy cluster methods, that is C-Means and Fuzzy C-Means. Both methods have difference on membership value and weighted membership value. The result of this research showed that Fuzzy C-Means was more robust than C-Means.