@article {10.3844/jcssp.2018.81.91,
article_type = {journal},
title = {Multiview Hierarchical Agglomerative Clustering for Identification of Development Gap and Regional Potential Sector},
author = {Munandar, Tb Ai and Azhari, and Musdholifah, Aina and Arsyad, Lincolin},
volume = {14},
number = {1},
year = {2018},
month = {Jan},
pages = {81-91},
doi = {10.3844/jcssp.2018.81.91},
url = {https://thescipub.com/abstract/jcssp.2018.81.91},
abstract = {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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}