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If we run t-SNE with a too small perplexity such as 20, we get more of these patterns that do not exist: This will cluster e.g. with DBSCAN, but it will yield four clusters. … In: Proceedings of the 10th International Conference on Similarity Search and Applications (SISAP), Munich, Germany. 2017 First, we have this input data: As you may guess, this is derived from a "color …

Two most dissimilar cluster members can happen to be very much dissimilar in comparison to two most similar. Single linkage method controls only nearest neighbours similarity. … The metaphor of this build of cluster is type. …

Is there a specific purpose in terms of efficiency or functionality why the k-means algorithm does not use for example cosine (dis)similarity as a distance metric, but can only use the Euclidean norm? … "(Non)Euclidean distance" may concern distance between two data points or distance between a data point and a cluster centre. Both ways have been attempted to address in the answers so far.] …

$\mathbf{w^T}$ is the transpose of $\mathbf{w}$, and $\|\mathbf{w}\| = \mathbf{w}^T\mathbf{w}$. Let: $\mathbf{x}$ be a feature vector (i.e., the input of the SVM). … cluster=469291847682573606&hl=en&as_sdt=0,22 ; http://www.chapelle.cc/olivier/pub/neco07.pdf …

the three clusters (krange) were dissolved across the samples, and the average clusterwise Jaccard similarity is > 0.95 for all clusters. … gamma, within sum of squares) for a range of cluster numbers (e.g., 2 to 10), repeat 100 or 500 times, and look at the boxplot of your statistic as a function of the number of cluster. …

I can imagine of a number dimensions or aspects of "rightness" of this or that clustering method: Cluster metaphor. … Each clustering algorithm or subalgorithm/method implies its corresponding structure/build/shape of a cluster. …

You estimate the treatment effect using a simple linear regression of the outcome on the treatment and the covariates, including the matching weights in the regression and using a cluster-robust standard … This all centers on covariate balance, the similarity between covariate distributions across the treatment groups. …

It works on anything you can define the pairwise similarity on. Which you can get by multiplying the Levenshtein distance by -1. … cluster they are exemplar of): have: chances, edit, hand, have, high following: following problem: problem I: I, a, at, etc, in, list, of possibly: possibly cluster: cluster word: For, and, for, long …

The facets of Gower similarity ($GS$): When all variables are quantitative (interval) then the coefficient is the range-normalized Manhattan distance converted into similarity. … A general coefficient of similarity and some of its properties // Biometrics, 1971, 27, 857-872 $^2$ Podani, J. …

I could use the word embedding to cluster the vocabulary of all words into a fixed set of clusters, say, 1000 clusters, where I use cosine similarity on the vectors as a measure of word similarity. … that are part of cluster $i$. …

I need to cluster this word list, such that similar words, for example words with similar edit (Levenshtein) distance appears in the same cluster. … For example "algorithm" and "alogrithm" should have high chances to appear in the same cluster. …

What would be the approach to use Dynamic Time Warping (DTW) to perform clustering of time series? … I have read about DTW as a way to find similarity between two time series, while they could be shifted in time. Can I use this method as a similarity measure for clustering algorithm like k-means? …

To compare the similarity of two hierarchical (tree-like) structures, measures based on cophenetic correlation idea are used. … If after the above precautions you continue to think that you want a measure of similarity between hierarchical classifications you might google on 'comparing of dendrograms' and 'comparing hierarchical …

The algorithm is called Clara in R, and is described in chapter 3 of Finding Groups in Data: An Introduction to Cluster Analysis. by Kaufman, L and Rousseeuw, PJ (1990). … In R you can take a look at the package cluster. All described algorithms are implemented there. See ?pam, ?clara, ?hclust, ... Check also the different implementation of the algorithm in ?kmeans. …

The graph in question has approximately 3 million edges and each edge expresses the degree of similarity between the two vertices it connects. … In particular, in this dataset edges are individuals and vertices are a measure of the similarity of their observed behavior. …