  The identification of regional development gaps is an effort to see how far
the development conducted in every District in a Province. By seeing the gaps
occurred, it is expected that the Policymakers are able to determine which
region that will be prioritized for future development. Along with the regional
gaps, the identification in Gross Regional Domestic Product (GRDP) sector is
also an effort to identify the achievement in the development in certain fields
seen from the potential GRDP owned by a District. There are two approaches that
are often used to identify the regional development gaps and potential sector,
Klassen Typology and Location Quotient (LQ), respectively. In fact, the results
of the identification using these methods have not been able to show the
proximity of the development gaps between a District to another yet in a same
cluster. These methods only cluster the regions and GRDP sectors in a firm
cluster based on their own parameter values. This research develops a new
approach that combines the Klassen, LQ and hierarchical agglomerative
clustering (HAC) into a new method named multi view hierarchical agglomerative
clustering (MVHAC). The data of GRDP sectors of 23 Districts in West Java
province were tested by using Klassen, LQ, HAC and MVHAC and were then
compared. The results show that MVHAC is able to accommodate the ability of the
three previous methods into a unity, even to clearly visualize the proximity of
the development gaps between the regions and GRDP sectors owned. MVHAC clusters
23 districts into 3 main clusters, they are, Cluster 1 (Quadrant 1) consists of
5 Districts as the members, Cluster 2 (Quadrant 2) consists of 12 Districts and
Cluster 3 (Quadrant 4) consists of 6 Districts.
