Hadi’s Influence Measure and Identification of Regression Clusters Based on Potential Outliers
Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. Usually, clustering methods work based on some distance metric among the observation or it may use Co-variance and correlation structure among the variables. If all the given variables are depends on a single variable, then the procedure of clustering the observations is said to be regression clustering. In this paper, an iterative procedure of regression clustering method was proposed by using the famous Hadi’s influence measure. At each iteration, Chow’s F-test (1960) was used to check the discrimination between the clusters and the remaining observation. Moreover, control charts also used to graphically visualizes the iterations and the clustering process .Finally Chow’s test of equality of several regression equation helps firmly to establish the cluster discrimination and validity. This paper employed this procedure for clustering 220 customers of a famous four-wheeler in India based on 19 different attributes of the four wheeler and its company.
Distance metric, Correlation structure, Hadi’s influence measure, Structural Break, Influential observation, Potential outlier cluster, Chow’s F- test
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