regards,
rimjhim

Please use that page instead of this one, even if I am leaving the comments here open.

that’s a well known problem and, unfortunately, since I have changed a lot of things in my life, I am still working on the new release of the code.

That means that you can actually use this code on Linux and Mac OS X systems only. Sorry.

Regarding the “suffix tree clustering algorithm” you’ve mentioned, what exactly do you mean? I never developed nothing related to such a name, I guess.

Regards,

Vincenzo.

PS: the official page of this software is now this one:

http://neminis.org/software/support-vector-clustering/

Sorry for the late. I was not able to connect for a week because I was not at home.

To be short, this is what I would do:

1. Run K-means several times with different ‘k’ values and choose the best instance. “Best” here means the instance that produce the best value for some validity index (C-index are supposed to be a good choice for K-means).

2. Run SVC several times with different parameters settings (kernel, C, q, etc.) and choose the best instance according to a validity index (the best choice is the index specifically developed for SVC, I guess).

3. Compare the results of the “best k-means instance” and the “best SVC instance”.

That’s all.

And no, I am sure that the validity index developed for the SVC does not fit the K-means because the index relies on specific characteristics of the SVC.

I hope I am of help.

Best,

VR.

I have already have some idea on the evaluation of clustering results now. As you mentioned, from a review of the literature, generally there are three approaches, internal criteria, external criteria, and relative criteria, have been used for the quantitative evaluation of clustering results in many of the research studies.

Internal criteria are the only means to evaluate the clustering quality of a completely new domain.

External criteria imply clustering evaluation by means of external pre-specified structure information of a dataset. Generally, several real-life or synthetic datasets with prior class information such as Iris data, Wisconsin’s breast cancer database, and Spam database, are used as evaluation benchmark for clustering results.

Relative criteria (also known as validity index) evaluate clustering results with different input parameter settings of a same clustering algorithm. A number of validity indices have been developed and proposed in literature.

If i want to compare the performance of K-means and SVC in a completely new domain without prior class information. There are two methods as follows:

1.In the process of running K-means and SVC, validity index is embeded respectively for parameter selection. When the optimal clusering resluts are found respectively, the clstering resluts are evaluated by internal criteria.

2.There is a validity index specific for SVC which is quite useful for parameter selection as you mentioned (I don’t know if it can used for K-means). If different validity indices are used in K-means and SVC, I think they can’t be compared. If the same validity index is used in both clustering algorithms, regardless of it is not appropriate for SVC, can the index value work as a crtieria of the performance of these two clustering algorithms?

Maybe I didn’t well express my question. I hope you can understand. I am a little confused here.

Many thanks and Best regards

Lawrrence

Thank you very very much. It seems clear to me. I’d like to read relevant parts of your thesis and the paper suggested by you first. You really give me a great inspiration on how to evaluate the quality of clustering results when datasets are unlabeled. It’s great appreciated for your help.

Best Regards

Lawrence

welcome back.

Since your datasets are unlabeled, the first thing you need are some criteria to evaluate the quality of clustering results. The most effective way to evaluate clustering results when data are unlabeled, is a relative criterion (aka validity index).

In the section 3.3.3 of my thesis I present some of classical validity indices and in

J. Wang and J. Chiang, “A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm,” Pattern Recognition, vol. 41, iss. 2, pp. 506-520, 2008.

you can found a validity index specific for SVC. I used it to develop a new stopping criterion for SVC (not available in the software you are using).

So, you have to run the SVC and K-means several times, each with a different parameter settings and then evaluate the results. The better index value, the better the clustering. Since you probably don’t know the number of clusters and K-means need it, a classical way is to run K-means with different number of clusters in input and then choose the instance that yields the best validity index value. As far as the SVC is concerned, you can try different combinations of q/C/kernel and choose the instance that yields the best value of the validity index.

I hope I was clear.

Best,
VR.

I notice that you compared the performance of different clustering methods using real-life benchmarks such as Iris data, Wisconsin’s breast cancer database, and Wine Recognition Database in your thesis. Camastra (2005) also compared the performance of the current clustering methods including K-means, Neural Gas, Self-Organizing Map (SOM), Spectral clustering algorithm and SVC on three kinds of real-life benchmarks. However, there already are class information for these data sets, that is, we have already know the class each observation belongs. It’s quite OK and necessary that these datasets are used to compare the results of clustering methods. If I want to compare the performance of SVC and K-means on a dataset without class information ahead, do you have some suggestions?

Many thanks and Best Regards

Lawrence