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The identification of regional development gaps is an effort to see how farthe development conducted in every District in a Province. By seeing the gapsoccurred, it is expected that the Policymakers are able to determine whichregion that will be prioritized for future development. Along with the regionalgaps, the identification in Gross Regional Domestic Product (GRDP) sector isalso an effort to identify the achievement in the development in certain fieldsseen from the potential GRDP owned by a District. There are two approaches thatare often used to identify the regional development gaps and potential sector,Klassen Typology and Location Quotient (LQ), respectively. In fact, the resultsof the identification using these methods have not been able to show theproximity of the development gaps between a District to another yet in a samecluster. These methods only cluster the regions and GRDP sectors in a firmcluster based on their own parameter values. This research develops a newapproach that combines the Klassen, LQ and hierarchical agglomerativeclustering (HAC) into a new method named multi view hierarchical agglomerativeclustering (MVHAC). The data of GRDP sectors of 23 Districts in West Javaprovince were tested by using Klassen, LQ, HAC and MVHAC and were thencompared. The results show that MVHAC is able to accommodate the ability of thethree previous methods into a unity, even to clearly visualize the proximity ofthe development gaps between the regions and GRDP sectors owned. MVHAC clusters23 districts into 3 main clusters, they are, Cluster 1 (Quadrant 1) consists of5 Districts as the members, Cluster 2 (Quadrant 2) consists of 12 Districts andCluster 3 (Quadrant 4) consists of 6 Districts.

Multiview Hierarchical Agglomerative Clustering for Identification of Development Gap and Regional Potential Sector