What is the problem of clustering?
There are a number of problems with clustering. Among them: dealing with large number of dimensions and large number of data items can be problematic because of time complexity; the effectiveness of the method depends on the definition of “distance” (for distance-based clustering).
What are the assumptions of cluster analysis?
The choice of clustering variables is also of particular importance. Generally, cluster analysis methods require the assumption that the variables chosen to determine clusters are a comprehensive representation of the underlying construct of interest that groups similar observations.
How is k-means used in cluster analysis?
K-means is a widely used method in cluster analysis. In my understanding, this method does NOT require ANY assumptions, i.e., give me a dataset and a pre-specified number of clusters, k, and I just apply this algorithm which minimizes the sum of squared errors (SSE), the within cluster squared error.
Is the prior probability for all k clusters the same?
the prior probability for all k clusters is the same, i.e., each cluster has roughly equal number of observations; If any one of these 3 assumptions are violated, then k-means will fail. I could not understand the logic behind this statement. I think the k-means method makes essentially no assumptions,…
What is the assumption of independent and identically distributed error terms?
The assumption of independent and identically distributed error terms with an expected value of zero and a constant variance also implies a constant variance in the cross-section. That means, each error term has the same finite variance.
Which is the best tool to do cluster analysis?
If you want to do cluster analysis as in discover structure then k-means is IMHO not the best choice. It tends to cluster when there are not clusters, and it cannot recognize various structures you do see a lot in data. Fine print: all images were generated with ELKI.