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OPTICS in Weka is pretty much an external application. It's student code an unmaintained. Weka is good for classification, anything else is of very limited use. … In general, for clustering and outlier detection it is miles ahead of Weka - kind of the "Weka for clustering and outlier detection". …

Many clustering algorithms only work with continuous values. Therefore, they are greyed out until you convert all your attributes to numeric. But beware that the methods will still assume e.g. a simi …

You can run all Weka algorithms from command line instead of using the GUI. But I doubt this will ultimately resolve your memory problems. … Nor will using Java - you won't save any memory by calling it from Java as opposed to the Weka command line. …

If I were a reviewer, I would reject such results as extremely implausible. Confusion matrixes are also not commonly used with clustering, because of the correspondence problem: how do you know which …

There is no rule of thumb, as the values are not comparable across data sets. Not even across different normalizations of the data set or across algorithms. You can mostly use them to compare differen …

Correlation does not imply the attributes are bad. It is never a good idea to remove attributes just because of some number. Rather use these to guide you to understand your data. I.e. look at highl …

P.S. instead of Weka, you may want to use ELKI which is much more powerful for clustering. …

Obviously the mean squared error is the total sum of errors divided by the number of instances n, the number of variables p, or their product n*p. You can easily compute either with a calculator. Bew …

k-means won't work for this task. In fact I believe none of the algorthms will without gigabyes of data describing "common knowledge". Much of the semantic web is motivated by the observation that y …