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

by   T. A. Munandar, et al.

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.



There are no comments yet.


page 1

page 6

page 8

page 9

page 10


Bayesian regional flood frequency analysis for large catchments

Regional flood frequency analysis is commonly applied in situations wher...

Bayesian regional food frequency analysis for large catchments

Regional flood frequency analysis is commonly applied in situations wher...

Robust Clustering with Subpopulation-specific Deviations

The National Birth Defects Prevention Study (NBDPS) was a case-control s...

Fuzzy-Klassen Model for Development Disparities Analysis based on Gross Regional Domestic Product Sector of a Region

Analysis of regional development imbalances quadrant has a very importan...

Distance-based phylogenetic inference from typing data: a unifying view

Typing methods are widely used in the surveillance of infectious disease...

Regional Seismic Information Entropy for Detecting Precursors of Earthquake Activation

Here a method is presented for detecting precursors of earthquakes from ...

maskSLIC: Regional Superpixel Generation with Application to Local Pathology Characterisation in Medical Images

Supervoxel methods such as Simple Linear Iterative Clustering (SLIC) are...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.