Hi all, I am very confusing on clustering.
My problem facing now: there are multiple clustering types and methods in hierarchical clustering, and I don’t which method is suitable for my experiment.
I want to know the differences between different linkage methods (simple, group average, ward’s linkage, etc.) and also distance methods (e.g. range, standard deviations, manhattan, etc.).
My data have 614 data in total. Someone suggested that it is less 800s, which not suitable to do hierarchical clustering, and the reliability is low. Is it true?
If so, what method I can used to do the grouping? K-mean clustering? repeated anova?
Moreover, my data is time -related, which is collected in 4 months in a year. Someone suggested me to do time de-trend, but I worried that it will lose my target. Since I want to group the items together by their growing trends.
Please anyone can help?
My problem facing now: there are multiple clustering types and methods in hierarchical clustering, and I don’t which method is suitable for my experiment.
I want to know the differences between different linkage methods (simple, group average, ward’s linkage, etc.) and also distance methods (e.g. range, standard deviations, manhattan, etc.).
My data have 614 data in total. Someone suggested that it is less 800s, which not suitable to do hierarchical clustering, and the reliability is low. Is it true?
If so, what method I can used to do the grouping? K-mean clustering? repeated anova?
Moreover, my data is time -related, which is collected in 4 months in a year. Someone suggested me to do time de-trend, but I worried that it will lose my target. Since I want to group the items together by their growing trends.
Please anyone can help?