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Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw
Publisher: Wiley-Interscience

The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a KAUFMAN L and Rousseeuw P J (1990) Finding Groups in Data: an Introduction to Cluster Analysis, John Wiley & Sons. Cluster analysis is called Q-analysis (finding distinct ethnic groups using data about believes and feelings1), numerical taxonomy (biology), classification analysis (sociology, business, psychology), typology2 and so on. Finding Groups in Data: An Introduction to Cluster Analysis (Wiley. Kogan J., Nicholas C., Teboulle M. Knowledge Discovery and Data Mining (PAKDD. Finding Groups in Data: An Introduction to Cluster Analysis. Publications on Spatial Database and Spatial Data Mining at UMN . Hierarchical Cluster Analysis Some Basics and Algorithms 1. Clustering Large and High Dimensional data. Cluster analysis is a collection of statistical methods, which identifies groups of samples that behave similarly or show similar characteristics. €� John Wiley & Sons, 1990 Collective Intelligence. Introduction 1.1 What is cluster analysis?