Comparison of Cluster and Linkage Validity Indices in Integrated Cluster Analysis with Structural Equation Modeling War-PLS Approach
This study wants to compare the Integrated Cluster and SEM models of the Warp-PLS approach with various cluster and linkage validity indices on Service Quality, Environment, Fashions, Willingness to Pay, and Compliant Behavior of Bank X Customer Paying Behavior. The data used in this study are primary. The variables used in this study are service quality, environment, fashion, willingness to pay, and compliance with paying behavior at Bank X. The data were obtained through a questionnaire with a Likert scale — the measurement of variables in primary data using the average score of each item. The sampling technique used was purposive sampling. The object of observation is the customer as many as 100 respondents. Data analysis was carried out quantitatively; the descriptive study was carried out first, then Integrated Cluster analysis and SEM analysis of the Warp-PLS approach were carried out with euclidean distances on 4 cluster validity indices, including Index Sillhouette, Krzanowski-Lai, Dunn, Davies-Bouldin, as well as on various linkages (Ward, Average, and Complete Linkage). This research uses R software. The results show that the Silhouette, Krzanowski-Lai, Dunn, and Davies-Bouldin indexes, the complete linkage method is better than the ward and average linkage methods. The novelty in this research is Integrated Cluster Analysis and SEM of the Warp-PLS approach to compare 4 cluster validity indices and three linkages.
Keywords: Cluster Analysis; SEM; Warp-PLS; Integration Model; Dummy Variable; Cluster Validity Index; Linkage
SOLIMUN M. S. Structural Equation Modeling (SEM) with LISREL and AMOS. Malang: Faculty of Mathematics and Natural Sciences, Brawijaya University, 2002.
SUPRANTO J. Multivariate analysis of meaning and interpretation. Jakarta: PT Rineka Cipta, 2004.
SOLIMUN M. S. Multivariate analysis of structural modeling. Malang: CV Image of Malang, 2010.
HAIR J.F., ANDERSON R.E., TATHAM R.L. et al. Multivariate data analysis. 5th edition. Jakarta: Gramedia Main Library, 2006.
LIU Q., ZHANG R., HU R. et al. An improved path-based clustering algorithm. Knowledge-Based Systems, 2018, 163. doi: 10.1016/j.knosys.2018.08.012.
JAIN A.K., AMBASSADOR R.C. Algorithms for data clustering. New Jersey: Prentice-Hall, 1988.
MATJIK A., SUMERTAJAYA I. Perancangan dan Percobaan dengan Aplikasi SAS dan Minitab. (Design and Experimentation with SAS and Minitab Applications). 2nd ed. Bogor: IPB Press, 2002.
JOHNSON R.A., WICHERN D.W. Applied multivariate analysis. 3rd ed. New Jersey: Prentice-Hall, 1992.
FU W., PERRY P. O. Estimating the number of clusters using cross-validation. Journal of Computational and Graphical Statistics, 2020, 29(1): 162-173.
JAIN A. K., DUBER R. C. Algorithms for clustering data. New Jersey: Prentice-Hall, 1988.
PATIBANDLA R. L., VEERANJANEYULU, N. Performance analysis of partition and evolutionary clustering methods on various cluster validation criteria. Arabian Journal for Science and Engineering, 2018, 43(8):379-439.
CHARRAD M., GHAZZALI N., BOITEAU V. et al. Package 'nbclust'. Journal of Statistical Software, 2014, 61 (6): 1-36.
VOICU A., DUTEANU N., VOICU M., et al. The rock and cluster R packages applied to drug candidate selection. Journal of Cheminformatics, 2020, 12(1): 1-8.
KRAZANOWSKI W. J. & LAI Y.T. A criterion for determining the number of groups in a data set using a sum of squares clustering. Biometrics, 1988, 44: 23–34.
GOODMAN L. A., & KRUSKAL W. H. Measures of association for cross classifications. Journal of the American Statistical Association, 1954, 49: 732–769.
BOLSHAKOVA N. & AZUAJE F. Cluster validation techniques for genome expression data. Signal Processing, 2003, 83 (4): 825-833.
AZUAJE F., BOLSHAKOVA N. Clustering genomic expression data: Design and evaluation principles. In: BERRAR D.P., DUBITZKY W., GRANZOW M. (Eds.) A practical approach to microarray data analysis. Boston, MA: Springer, 2003. DOI: 10.1007/0-306-47815-3_13.
WANI M.A. & RIYAZ R. A novel point density-based validity index for clustering gene expression datasets. International Journal of Data Mining and Bioinformatics, 2017, 17 (1): 66-84.
BATES A. & KALITA J. Counting clusters in Twitter posts. In: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (ICTCS '16). New York: Association for Computing Machinery, 2016, Article 85: 1–9. DOI: 10.1145/2905055.2905295.
RUZ G. A., HENRÍQUEZ P. A., & MASCAREÑO A. Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 2020, 106: 92-104.
WANG J. & WANG X. Structural equation modeling: Applications using Mplus. New York: John Wiley & Sons, 2019.
SOLIMUN F., NURJANNAH N. Multivariate Statistical Method: Structural Equation Modeling Based on WarpPLS, 0. 90. Malang: UB Press, 2017.
GHOZALI I. Structural equation modeling: An alternative method with partial least squares (PLS). Semarang: Diponegoro University Publishing Agency, 2008.
RAMYA, N., KOWSALYA, A., & DHARANIPRIYA, K. Service quality and its dimensions. EPRA International Journal of Research & Development. 2019, 4: 38-41.
SOERIAATMADJA R.E. Environmental Science. Bandung: ITB Publisher, 1997.
ROBBINS S.P. Organizational behavior: controversy, concept, application. Jakarta: Prehalinda, 2002.
HASS J. K. Economic sociology: An introduction. Abingdon: Routledge, 2020.
GOLIN J. & DELHAISE P. The bank credit analysis handbook: a guide for analysts, bankers, and investors. New York: John Wiley & Sons, 2013.
PALACÍN-SÁNCHEZ M. J., CANTO-CUEVAS F. J., & DI-PIETRO F. Trade credit versus bank credit: a simultaneous analysis in European SMEs. Small Business Economics, 2019, 53(4): 1079-1096.
PERMADI T., NASIR A. & ANISMA Y. Study of willingness to pay taxes on individual taxpayers who do independent work (the case at KPP Pratama Tampan Pekanbaru). Journal of Economics, 2013, 21 (02):43-52.
BUDIONO D. The behavior of corporate taxpayers in fulfilling tax obligations: Humanistic theory perspective. Jakarta: FEBSOS, 2016.
BOCCIA F., MALGERI M. R. & COVINO D. Consumer behavior and corporate social responsibility: An evaluation by a choice experiment. Corporate Social Responsibility and Environmental Management, 2019, 26(1), 97-105.
SAHU R. A gap analysis of enforcing FCPA-compliant codes of conduct in low-and middle-income countries. Business Law Review, 2020, 41(6):56-64.
SIAT C.C., TOLY A.A. Factors affecting taxpayer compliance in fulfilling tax obligations in Surabaya. Tax & Accounting Review, 2013, 1(1): 41.
SUBROTO B. The influence of attitudes, subjective norms, perceived behavior control, and sunset policy on taxpayer compliance with intention as an intervening variable. Melville, NY: JMP Press, 2010
MUNADJAT D. Environmental law (book v: sectoral): Indonesian environmental law (in the national & international system). Bandung: Binacipta, 1984.
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