Cluster Analysis of Business Data

• Author(s): Sousa, Áurea ; Bacelar-Nicolau, Helena ; Silva, Osvaldo
• Date: 2014
• Persistent ID: http://hdl.handle.net/10400.3/2852
• Origin: Repositório da Universidade dos Açores
• Subject(s): Cluster Analysis; Affinity Coefficient; VL Methodology; Complex Data; Global Statistics of Levels
• Document Type: Article
• Language: English

Description

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. In this work, classical as well as probabilistic hierarchical clustering models are used to look for typologies of variables in classical data, typologies of groups of individuals in a classical three-way data table, and typologies of groups of individuals in a symbolic data table. The data are issued from a questionnaire on business area in order to evaluate the quality and satisfaction with the services provided to customers by an automobile company. The Ascendant Hierarchical Cluster Analysis (AHCA) is based, respectively, on the basic affinity coefficient and on extensions of this coefficient for the cases of a classical three-way data table and a symbolic data table, obtained from the weighted generalized affinity coefficient. The probabilistic aggregation criteria used, under the probabilistic approach named VL methodology (V for Validity, L for Linkage), resort essentially to probabilistic notions for the definition of the comparative functions. The validation of the obtained partitions is based on the global statistics of levels (STAT).