Comparison of hierarchical cluster analysis methods by cophenetic correlation
By Sinan Saraçlı, N. Doğan, İ. Doğan. Journal of Inequalities and Applications, 2013
336 citations recorded by the source corpus.
This study proposes the best clustering method(s) for different distance measures under two different conditions using the cophenetic correlation coefficient. In the first one, the data has multivariate standard normal distribution without outliers for n=10,50,100 and the second one is with outliers (5%) for n=10,50,100. The proposed method is applied to simulated multivariate normal data via MATLAB software. According the results of simulation the Average (especially for n=10) and Centroid (especially for n=50 and n=100) methods are recommended at both conditions. This study hopes to contribute to literature for making better decisions on selection of appropriate cluster methods by using subgroup sizes, variable numbers, subgroup means and variances.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/08271351a33370d43d3d21fafe1e5fa93a86ac06
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In Artificial Intelligence
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Citations in the corpus
- Electric vehicle charging demand forecasting model based on big data technologies (2016, methodology): “This paper used an average linkage algorithmwhich treats the distance between two clusters as an average distance between all pairs of samples where a member of a pair belongs to each cluster [30].”