Article: Two-stage graph-clustering algorithm and localised classification model to identify apt business locale Journal: International Journal of Business Information Systems (IJBIS) 2015 Vol.20 No.2 pp.195 - 218 Abstract: Previous studies conducted in the economy, finance and business management fields have found that there exists a collection of business agglomerations, which contain various numbers of firms that are spread on a specific region. Based on this realness, the selection of apt business locale for a new establishment should then be considered as a trial to identify the prospective business agglomeration in which the new establishment would be able to compete with existing firms. Consequently, a pertinent method that works by characterising the business agglomerations from a collection of business firm's data and subsequently computes the projection of business performance level of a new establishment in each identified agglomeration is developed in this study. A two-stage graph-clustering algorithm that purposively designed to unravel the task of business agglomerations identification is introduced, whereas the localised classification models perform the prediction of business performance level in each known agglomerations. Decisively, results from conducted experiment suggest that the proposed method is beneficial to distinguish the apt business locale for new establishments in a particular region with a collection of business agglomerations. Inderscience Publishers - linking academia, business and industry through research

Title: Two-stage graph-clustering algorithm and localised classification model to identify apt business locale

Authors: Harya Widiputra; Marsudi Kisworo; Agnes Novita; Tiolina Pardede

Addresses: Faculty of Information Technology, Perbanas Institute, Indonesia ' Faculty of Information Technology, Perbanas Institute, Indonesia ' Faculty of Information Technology, Perbanas Institute, Indonesia ' Faculty of Information Technology, Perbanas Institute, Indonesia

Abstract: Previous studies conducted in the economy, finance and business management fields have found that there exists a collection of business agglomerations, which contain various numbers of firms that are spread on a specific region. Based on this realness, the selection of apt business locale for a new establishment should then be considered as a trial to identify the prospective business agglomeration in which the new establishment would be able to compete with existing firms. Consequently, a pertinent method that works by characterising the business agglomerations from a collection of business firm's data and subsequently computes the projection of business performance level of a new establishment in each identified agglomeration is developed in this study. A two-stage graph-clustering algorithm that purposively designed to unravel the task of business agglomerations identification is introduced, whereas the localised classification models perform the prediction of business performance level in each known agglomerations. Decisively, results from conducted experiment suggest that the proposed method is beneficial to distinguish the apt business locale for new establishments in a particular region with a collection of business agglomerations.

Keywords: business location; graph clustering; localised classification model; business agglomeration; business clusters; business performance levels; modelling; facilities location.

DOI: 10.1504/IJBIS.2015.071537

International Journal of Business Information Systems, 2015 Vol.20 No.2, pp.195 - 218

Received: 09 Apr 2014
Accepted: 10 Jul 2014
Published online: 31 Aug 2015 *

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Title: Two-stage graph-clustering algorithm and localised classification model to identify apt business locale

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