Cluster Analysis of Business Data
Keywords:Cluster Analysis, Affinity Coefficient, VL Methodology, Complex Data, Global Statistics of Levels.
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).
Bacelar-Nicolau, H., â€œContributions to the Study of Comparison Coefficients in Cluster Analysisâ€, PhD Thesis (in Portuguese), Universidade de Lisboa, 1980.
Bacelar-Nicolau, H., "The affinity coefficient in cluster analysis", Methods of Operations Research, vol. 53, Martin J. Bekmann et al (ed.), Verlag Anton Hain, Munchen, pp. 507-512, 1985.
Bacelar-Nicolau, H., "On the distribution equivalence in cluster analysisâ€, In Proceedings of the NATO ASI on Pattern Recognition Theory and Applications, Springer - Verlag, New York, pp. 73-79, 1987.
Bacelar-Nicolau, H., â€œTwo Probabilistic Models for Classification of Variables in Frequency Tablesâ€, In: Bock, H.-H. (Eds.), Classification and Related Methods of Data Analysis, North Holland, pp. 181-186, 1988.
Bacelar-Nicolau, H., â€œThe Affinity Coefficientâ€, In: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, H.-H. Bock and E. Diday (Eds.), Series: Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, Berlin, pp. 160-165, 2000.
Bacelar-Nicolau, H., â€œOn the Generalised Affinity Coefficient for Complex Dataâ€, Biocybernetics and Biomedical Engineering, vol. 22, no. 1, pp. 31-42, 2002.
Bacelar-Nicolau, H.; Nicolau, F.C.; Sousa, Ã.; Bacelar-Nicolau, L., â€œMeasuring Similarity of Complex and Heterogeneous Data in Clustering of Large Data Setsâ€, Biocybernetics and Biomedical Engineering, vol. 29, no. 2, pp. 9-18, 2009.
Bacelar-Nicolau, H.; Nicolau, F.C.; Sousa, Ã.; Bacelar-Nicolau, L., â€œClustering Complex Heterogeneous Data Using a Probabilistic Approachâ€, In Proceedings of Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2010), pp. 85-93, 2010 (electronic publication) .
Bock, H.-H. and Diday, E., Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, Series: Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, Berlin, 2000.
Burns, Robert and Burns, Richard, Business Research Methods and Statistics using SPSS, SAGE Publications Ltd, 2008.
Doria, I.; Sousa, Ã; Bacelar-Nicolau, H., Le CalvÃ©; G.Â¬Â¬Â¬Â¬Â¬Â¬Â¬Â¬, â€œComparison of Modal Variables Using Multivariate Analysisâ€, In: Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications, Studies in Theoretical and Applied Statistics, JoÃ£o Lita da Silva, Frederico Caeiro, Isabel NatÃ¡rio and Carlos A. Braumann (Eds.), Springer, Berlin, Heidelberg, pp.363-370, 2013.
Esposito, F., Malerba, D., Tamma, V., â€œDissimilarity Measures for Symbolic Objectsâ€, In: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, H.-H. Bock and E. Diday (Eds.),: Springer-Verlag, Berlin, pp. 165-185, 2000.
Lerman, I. C., â€œSur l`Analyse des DonnÃ©es PrÃ©alable Ã une Classification Automatique (Proposition dâ€™une Nouvelle Mesure de SimilaritÃ©) â€, Rev. MathÃ©matiques et Sciences Humaines, vol. 32, no. 8, pp. 5-15, 1970.
Lerman, I. C., Classification et Analyse Ordinale des DonnÃ©es, Dunod, Paris, 1981.
Matusita, K., â€œOn the Theory of Statistical Decision FuncÂ¬tionsâ€, Ann. Instit. Stat. Math, vol. III, pp. 1-30, 1951.
Nicolau, F.C., â€œCluster Analysis and Distribution Functionâ€, Methods of Operations Research, vol. 45, pp. 431-433, 1983.
Nicolau, F.C. and Bacelar-Nicolau, H., â€œSome Trends in the Classification of Variablesâ€, In: Hayashi, C., Ohsumi, N., Yajima, K., Tanaka, Y., Bock, H.-H., Baba, Y. (Eds.), Data Science, Classification, and Related Methods. Springer-Verlag, pp. 89-98, 1998.
Nicolau, F.C. and Bacelar-Nicolau, H., â€œClustering Symbolic Objects Associated to Frequency or Probability Laws by the Weighted Affinity Coefficientâ€, In: Applied Stochastic Models and Data Analysis, Quantitative Methods in Business and Industry Society (ASMDA99), H. Bacelar-Nicolau, F.C. Nicolau and Jacques Janssen (Eds.), INE, Lisboa, Portugal, pp. 155-158, 1999.
Premkanth, P., â€œMarket Segmentation and Its Impact on Customer Satisfaction with Especial Reference to Commercial Bank of Ceylon PLCâ€, Global Journal of Management and Business Research (GJMBR), vol. 12, no. 17, Version 1.0, 2012.
Sousa, Ã., â€œContributions to the VL Methodology and Validation Indexes for Data of Complex Natureâ€, PhD Thesis (in Portuguese), Universidade dos AÃ§ores, 2005.
Sousa, Ã., Nicolau, F., Bacelar-Nicolau, H., Silva, O., â€œWeighted Generalised Affinity Coefficient in Cluster Analysis of Complex Data of the Interval Typeâ€, Biometrical Letters, vol. 47, no. 1, pp. 45-56, 2010.
Sousa, Ã., Silva, O., Bacelar-Nicolau, H., Nicolau, F.C., â€œDistribution of the Affinity Coefficient between Variables based on the Monte Carlo Simulation Methodâ€, Asian Journal of Applied Sciences, vol. 1, no. 5, pp. 236-245, 2013a.
Sousa, Ã., TomÃ¡s, L, Silva, O., Bacelar-Nicolau, H., â€œSymbolic Data Analysis for the Assessment of User Satisfaction: an Application to Reading Rooms Servicesâ€, European Scientific Journal (ESJ), June 2013/Special/Edition no. 3, pp. 39-48, 2013.
How to Cite
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.