Use of GIS and association rule mining in guiding strategic business expansion planning:case study Chase Bank (K) Ltd
In recent years, optimal site selection has become one of the main concerns for managers of business enterprises. In addition, various kinds of spatial and non-spatial parameters influence the efficiency of new branches. These factors have a direct relation with site selection indicators. In this research project, the use of Geographic Information Systems (GIS) and Data Mining (DM) to determine and extract useful knowledge not only to help managers make better decisions for site selection, but also for extracting associations between selected parameters is investigated. The study also attempted to find a link between a mathematically determined efficiency measure and spatial/general association rules, which is a database method in data mining. During the research, the study area was classified into three different classes as ‘high’, ‘average’, and ‘low’ according to the efficiency and turnover measures. Afterwards, in each class an a priori like algorithm was used to establish the most frequent item sets and predict an average range of efficiency. In general, as the efficiency measure in the low class had a higher frequency than in other classes, negative rules were obtained rather than positive rules. In addition, the association rules for the small scale gave more meaningful results than those of the large scale. The reason was in the use of real parameters instead of aggregated parameters. The usability of this method was not absolutely good with this data set and it is recommended that normal distributed efficiency measure data be employed to find association rules in all the classes. Finally, for the site selection issues, the managers can use this method as a comparison factor, among different candidate areas. They can rely on the validation measures such as support, confidence, lift and leverage to select the best location for a new site.