[1] Meng, H. D., Song, Y. C., Song, F. Y., and Shen, H. T. (2011). Research and application of cluster and association analysis in geochemical data processing. Comput. Geosci, 15(1), 87–98.
[2] Gazley, M. F., Collins, K. S., Roberston, J., Hines, B. R., Fisher, L. A., & McFarlane, A. (2015). Application of principal component analysis and cluster analysis to mineral exploration and mine geology. In AusIMM New Zealand Branch Annual Conference.
[3] Charrad, M., Ghazzali, N., Boiteau, V., and Niknafs, A. (2014). NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J. Stat. Softw, 61(i06), 1-36.
[4] Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. J. Cybern, 4(1), 95–104.
[5] Milligan G. W., and Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179.
[6] Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math, 20(1), 53–65.
[7] Tibshirani, R., Walther, G., and Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B (Statistical Methods), 63(2), 411–423.
[8] Zaremotlagh, S., Hezarkhani, A., and Sadeghi, M. (2016). Detecting homogenous clusters using whole-rock chemical compositions and REE patterns: A graph-based geochemical approach. J. Geochemical Explor., 170(1), 94–106.
[9] Golestan, F. D., Riabi, S. R. G., Majlesi, M. J., Memarzadeh, M., and Harooni, H. A. (2013). “Identification and Separation of Anomal Variable Using Correspondence and Discriminant Analyses Methods at Northern–Dalli Areae.” Journal of Analytical and Numerical Methods in Mining Engineering, 2(3): 35–43 (In Persian).
[10] Golestan, F. D., Riabi, S. R. G., Hezarkhani, A., Khalookakaei, A. R., Sakaki, S. H., and Harooni, H. A. (2016). “The Structure of Exploration Project Management by Spatial Geometry Methods for Separation Anomaly Using GERT Networking - A Case Study of Cu-Au Northern-Dally Porphyry.” Journal of Analytical and Numerical Methods in Mining Engineering, 6(11): 1–10 (In Persian).
[11] Caliński, T., and Harabasz, J. (1974). A dendrite method for cluster analysis. Commun. Stat. Methods, 3(1), 1–27.
[12] Duda, R. O., and Hart, P. E. (1973). Pattern classification and scene analysis. vol. 3, Wiley New York.
[13] Gordon, A. D. (1999). Classification. Monogr. Stat. Appl. Probab, vol. 82.
[14] Hubert, L. J., and Levin, J. R. (1976). A general statistical framework for assessing categorical clustering in free recall. Psychol. Bull, 83(6), 1072-1080.
[15] Baker, F. B., and Hubert, L. J. (1975). Measuring the power of hierarchical cluster analysis. J. Am. Stat. Assoc, 70(349), 31–38.
[16] Beale, E. M. L. (1969). Euclidean cluster analysis. Scientific Control Systems Limited.
[17] Sarle, W. S. (2003). SAS Technical report a-108, cubic clustering criterion. SAS Institute Inc.
[18] Milligan, G. W. (1980). An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 45(3), 325–342.
[19] Milligan, G. W. (1981). A monte carlo study of thirty internal criterion measures for cluster analysis. Psychometrika, 46(2), 187–199.
[20] Rohlf, F. J. (1974). Methods of comparing classifications. Annu. Rev. Ecol. Syst, 101–113.
[21] Davies D. L., and Bouldin, D. W. (1979). A cluster separation measure. IEEE Trans. Pattern Anal. Mach.Intell, 2(1), 224–227.
[22] Frey, T., and Van Groenewoud, H. (1972). A cluster analysis of the D2 matrix of white spruce stands in Saskatchewan based on the maximum-minimum principle. J. Ecol, 873–886.
[23] Hartigan, J. A. (1975). Clustering algorithms (probability & mathematical statistics). John Wiley & Sons Inc.
[24] Ratkowsky, D. A., and Lance, G. N. (1978). A criterion for determining the number of groups in a classification. Aust. Comput. J, 10(3), 115–117.
[25] Scott, A. J., and Symons, M. J. (1971). Clustering methods based on likelihood ratio criteria. Biometrics, 387–397.
[26] Marriott, F H. C. (1971). Practical problems in a method of cluster analysis. Biometrics, 501–514.
[27] Ball G. H., and Hall, D. J. (1965). ISODATA, a novel method of data analysis and pattern classification. DTIC Document.
[28] Friedman, H. P., and Rubin, J. (1967). On some invariant criteria for grouping data. J. Am. Stat. Assoc, 62(320), 1159–1178.
[29] McClain, J. O., and Rao, V. R. (1975). Clustisz: A program to test for the quality of clustering of a set of objects. Journal of Marketing Research. JSTOR, 456–460.
[30] Krzanowski, W. J., and Lai, Y. T. (1988). A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 23–34.
[31] Lebart, L., Piron, A., Labert, M., Morineau, A., and Piron, M. (2000). Statistique exploratoire multidimensionnelle. Dunod.
[32] Hubert, L., and Arabie, P. (1985). Comparing partitions. J. Classif, 2(1), 193–218.
[33] Halkidi, M., Vazirgiannis,M., & Batistakis, Y. (2000). Quality scheme assessment in the clustering process. In European Conference on Principles of Data Mining and Knowledge Discovery.
[34] Halkidi, M., and Vazirgiannis, M. (2001). Clustering validity assessment: Finding the optimal partitioning of a data set. In Proceedings IEEE International Conference on Data Mining.
[35] R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
[36] Aitchison, J. (1986). The statistical analysis of compositional data.