Hello,

We have been doing some data clustering to compare samples generated

by two different methods: A method is used to generate sample x_1,

then we cluster x_1 using diana in R package and determine the optimal

clustering scenario by maximizing calinsky harabasz index (as

calculated by R). diana is divisive analysis, which is a hierarchical

divisive clustering method. It computes a tree or dendrogram.

Our hypothesis is that one method should generate data which is less

scattered, meaning that cluster analysis should yield less number of

clusters.

However, when we do the clustering analysis on the generated samples,

we saw that there is no clear distinction between number of clusters.

But if I look at the tree's generated by diana then it is obvious to

me that the method which we expect to have less clusters has less

spread in the tree.

I am thinking that we should also use the variance of data in the

clusters in addition to number of clusters to compare the sampling

methods. I, however, could not find a theoretical way to do that.

Could you suggest me ideas, papers or books to follow up with this

problem?

I hope this makes sense.

Arif.

1. clustering-variance question

2. Question on Variances specially Duration Variance

We have just implemented Project Server2003.

i would like to know when does the Duration variance field (and other

variance fields for that matter) gets calculated by the system?

In the resource tab in the PWA, the system can provide you with a list of

tasks assigned to a resource including such info as when the task was last

updated and the duration variance of the task. To see how the data in that

view changes, I create a small project with tasks assigned to me. I noticed

that the value for the duration variance remain blank even after I approved

the updates I've submitted.

But when I added the Duration Variance column in the project plan in MS

Project, the column contains values for each task.

Can anyone explain the reason for differences for me?

Thank you very much in advance.

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