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histogramwekaclique

How to compute histograms using weka

Given a dataset with 23 points spread out over 6 dimensions, in the first part of this exercise we should do the following, and I am stuck on the second half of this:

  • Compute the first step of the CLIQUE algorithm (detection of all dense cells). Use three equal intervals per dimension in the domain 0..100,and consider a cell as dense if it contains at least five objects.

Now this is trivial and simply a matter of counting. The next part asks the following though:

  • Identify a way to compute the above CLIQUE result by only using the functions of Weka provided in the tabs of Preprocess, Classify , Cluster , or Associate . Hint : Just two tabs are needed.

I've been trying this for over an hour now, but I can't seem to get anywhere near a solution here. If anyone has a hint, or maybe a useful tutorial which gives me a little more insight into weka it would be very much appreciated!

Solution

  • I am assuming you have 23 instances (rows) and 6 attributes (dimensions)

    Use three equal intervals per dimension

    Use pre-process tab to discretize your data to 3 equal bins. See image or command line. You use 3 bins for intervals. You may choose to change useEqualFrequency to false and true and try again. I think true may give better results.

    weka.filters.unsupervised.attribute.Discretize -B 3 -M -1.0 -R first-last

    unsupervised.attribute.Discretize

    After that cluster your data. This will give show you near instances. Since you would like to find dense cells. I think SOM may be appropriate.

    a cell as dense if it contains at least five objects.

    You have 23 instances. Therefore try for 2x2=4 cluster centers, then go for 2x3=6,2x4=8 and 3x3=9. If your data points are near. Some of the cluster centers should always hold 5 instances no matter how many cluster centers your choose.