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Then I want to test how robust of the k-means can be; however, I haven't got more new data samples. My idea is: Take the first sample out and run k-means on the rest of 99 samples. … My question is how to measure the similarity of the 100 k-means results? I am thinking of get the statistics of silhouette coefficients. Does that make sense? Thanks. A. …

We need to cluster these queries in topics and we would like to find some unsupervised approach. for each query we have the search results. … I thought to use the number of urls in common as discriminants. Other discriminants could be the text similarity of queries and of titles of the results. …

The table for similarity of clusterings is below. First I calculated the Rand Index both manually with Excel and with "cluster_similarity" function in R and I got 63,4%. …

In other words, I want to train the weights to increase the rate of correct classifications based on nearest neighbour. … There are a numbers of issues here: (1) which distance metric is best to evaluate the similarity of two ordinal rankings, (2) how best to transform the pairwise Euclidean distances into a comparable ordinal …

Here is the idea: you split the data on train and test, build clustering on train part; use this clustering to cluster each of the element of the test set ( by the closest cluster); build a separate clustering … This similarity is the criterion of clustering quality. Now, how to measure this similarity is up to you. Once you get it, you select the subset of features to maximize this similarity. …

I would say that: clusters on n x p (along n) identify groups of observations (n) that tend in having similar values in their p features. clusters on n x n identify observations that have similar similarity … I can write these things up, but I'm not sure I'm grasping the deep meaning and implications of the differences. …

Usually when performing text document clustering, similarities across documents are assessed based on the lexical content of documents. … But, in my problem, I wish to consider both the lexical content and the ontology of documents while assessing similarities. I'll explain this with an example. …

I needed to cluster all my words into clusters(other task) and after some experiments i figured out, that with normalized embedding clusterization works better. … But for totally different(by meaning) words similarity is close to 1(for example: 'Football' and 'Phone'). Is there any reason for such behaviour of cosine_similarity measurement? …

Homogeneity of clusters can easily measure by calculating the sum of squared error (SEE): $$SSE = \sum_k \sum_{i \in c_k} \| x_i - \overline{c_k} \|^2$$ where $\overline{c_k}$ is the mean vector of cluster … Another idea to emasure cluster homogeneity is the following: $$H = \sum_k \sum_{i \in c_k} \sum_{j \in c_k, j \neq i} \| x_i - x_j \|^2$$ The measure $H$ reflects the pairwise similarity of the cluster …

To determine the optimal number of cluster, I obtain the the best cluster combination which maximizes the similarity of each member in one cluster and minimizes the similarity between clusters. … , it messed up with optimization strategy to determine the best number of cluster of my HAC algorithm. …

What is the best way of finding out the optimal number of clusters, given that I just have a similarity matrix? Is it possible to do it all in Scikit-learn without any extra implementation? …

So I'm looking for a method which will be able to give me a similarity measure between two set of clusters formed at different timestamps. … The idea mentioned here: https://math.stackexchange.com/questions/52184/measure-similarity-of-graphs is that of degree sequence and euclidean distance but works only on two graphs with matching number …

I've been collecting speech data for my baby brother (who is now 6 months old) with the intention of doing computational analysis of the development of his speech patterns. … I haven't much deep experience with ML/stats and was looking for some guidance as to what kind of signal processing (to clear out background noise, etc) as well as statistics procedures (clustering, cross-similarity …

Within a range of say 1-100 they can get some fairly precise metrical information but outside of that range they input greater or less than the limit values. … So if a user wants to know how similar one sample is to another, I could do some sort of hierarchical clustering. …

In my clustering problem I'm working with custom similarity measure and looking for any implementation of algorithms with asymmetrical distance or similarity matrix. … Taylor-Butina clustering/grouping for asymmetrical similarity. Here is the usage of it. Some R implementations, though not sure if it's usable with asymmetrical matrix. …