# Questions tagged [euclidean]

Euclidean distance is the intuitive notion of a 'straight-line' distance between two points in a Euclidean space.

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40 views

### Manhattan vs Euclidian Distance Measure [duplicate]

In which case we should pickup Manhattan distance and when we should use euclidian distance measure. To my understanding both are used for continues numeric data(not like cosine or others who works ...
25 views

### How to find the list of nearest vectors if ony a vector is given?

I know there are many ways to compute similarity of two different non-zero vectors but is it possible to get a list of nearest vectors whose values are continous given a single continous vector. Lets ...
124 views

### How Rapidminer handle same distance for KNN Algorithm

Actually I already asked in rapidminer forum, but no one has given an answer yet.. https://community.rapidminer.com/discussion/55963/how-k-nn-algorithms-work-with-same-distance-in-rapidminer#latest ...
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### Calculate Distance between two vectors and estimate goodness of fit to preestablished histogram shapes

I have a squared gene co-expression correlation matrix of many thousands of pairwise correlations among variables (10290^2 aprox). Each row/column represents a different gene and its pairwise ...
33 views

### the accuracy of covariance between two high-dimensional vectors

Question Is the covariance between high-dimensional vectors less accruate than covariance between two vectors in low-dimensional vecotrs? I am asking this questio to check if there is a need for '...
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### Is there any data distribution that can be separated by an euclidean classifier but not by a linear discriminant analysis?

So leaving out the mathematical part, i am just interested in a data distribution which can be separated by an euclidean classifer(which takes the center of mass of - and + class and there is a ...
61 views

### Difference between standardizing variables and using Mahalanobis distance

I am wondering how and/or why the Mahalanobis distance is different from using the Euclidean distance on standardized variables?
912 views

### Why is Kullback-Leilbler divergence a better metric for measuring distance between two probability distributions than squared error? [duplicate]

I know that KL-divergence is a metric that is more suitable when we want to measure the distance between numbers which a probability form. However, I am still confused what is the benefit of using KL-...
53 views

### Clustering high dimensional data

I was going through this wiki page on clustering in high dimensions and I don't understand the following statement there. Can someone explain to me what this means? The concept of distance becomes ...
205 views

### Difference between Euclidean ,Pearson, Geodesic and Mahalanobis distance metrics

Given a set of samples $X$. We are tasked to find an appropriate distance metric for $X$ from the given options which are Euclidean Pearson Geodesic and Mahalanobis distance metrics. To solve this, ...
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### Feature analyzing methods

I am a little confiused how to interpret following situation: I am trying to implement a image classification task using hog+SVM. For that i tried to analyze and understand the properties of the ...
34 views

### What is the specific normalization chi2 in seqdist?

In the documentation for the seqdist() function it is noted that there is "...a specific normalization for"CHI2" and "EUCLID". See the Details section." (p.60). But in the details section there is no ...
13 views

### Test difference of means between subgroups

I have a collection of N samples from which I have computed all pairwise Euclidean distances. The samples can be divided in 3 distinct subgroups. I want to test if the average distance of groups is ...
31 views

### Is my variable considered okay to use in k-means clustering with Euclidean distance?

I was wondering if I can use regular kmeans() in R with my variable "number of drug prescriptions" which equals a number between 1-25. From what I've read k-means ...
58 views

### Set similarity as weight to ratings

I have a problem deciding which similarity function to use. I want to find the similarity between the users based on their requirements about computer performance metrics normalized to 1. Each user ...
101 views

### What to do when results of hiearchical, k-means elbow, and k-means silhoutte disagree?

I am conducting a cluster analysis involving 60 subjects and 5 continuous variables. After appropriate scaling, I performed hierarchical clustering with Euclidean distance and complete linkage, and ...
162 views

### How to improve Pairwise Euclidean Distance for Similarity Measure

I am trying to identify the most similar stations between two DataFrames like below: ...
356 views

### Dot Product and Distance Matrix

If we want to calculate the squared distance between 2 vectors, $x$ and $y$, we use the dot product: $$||x-y||^2 = (x-y)(x-y)^T = xx^T - 2xy + yy^T$$ The question is, how to generalize this concept ...
478 views

### Is one-hot encoding and standardization of data equivalent to Gower's distance?

For clustering and other techniques for mixed data (numerical and categorical), Gower's distance is usually more preferred than Euclidean distance because the former computes distance differently for ...
192 views

### Clustering: choosing correlation-based over euclidean distance (fviz_dist)

I'm trying to cluster some business data. You can imagine the data as describing customers (age, no. of purchases, satisfaction after the first purchase, etc.). I work with normalised data. I also ...
97 views

### $L_2$ norm of product of two vectors

Let's assume we have two matrices $A^{d\times 1}$ and $B^{1 \times e}$, and we define their product as $C^{d\times e}$. Assuming $A,B$ are real valued with all entries in $[-1,1]$. I can intuitively ...
63 views

### Distance metric with characteristics of cosine and Manhattan

I'm working on a project where I want to find similarities between groups of events. So far I have expressed groups of events as vectors of event counts and computing similarities between them. I'm ...
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### Euclidean distances in spaces of different dimensions

A modest attempt to illustrate my question: Setup Consider a survey where you have to choose if you like to do an activity or not. You do this for a number of activities $A_1,...,A_8$. In addition, ...
394 views

### How k-means computes cluster centroids differently for each distance metric?

K-means computes cluster centroids differently for each distance metric. I don't know why the way of computing the centroid is dependent of the distance measure. I don't know how we compute the ...
54 views

### Similarity between 2 profiles (observations). Is it possible to generate a % similarity?

I have multiple profiles for 10 different people. Each person has been measured for 5 different continuous variables of different magnitudes. So my dataframe is 10x5 where each row represents a person ...
201 views

### Does it make sense to cluster asymmetric binary data with Ward's method?

I'm working with 104x42 data set where all variables are (asymmetric) binary (0-1). I've read that Ward's linkage method doesn't work theoretically properly with binary data beaucause it requests ...
674 views

### My neural network can't even learn Euclidean distance

So I'm trying to teach myself neural networks (for regression applications, not classifying pictures of cats). My first experiments were training a network to implement an FIR filter and a Discrete ...
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### What is the difference between these distances in self organized maps

I am building an anomaly model and am confused between these distances below. What is the difference between these distances in self organized maps. som.iris$distances dist(som.iris$codes[]) As ...
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### Hierarchical Cluster Analysis of 100 objects with 114 variables each

I'm intending to make a cluster analysis of 100 objects. I've read a couple of books and determined that a Hierarchical agglomerative procedure with Ward's linkage method should be used in my case. As ...
332 views

### Obtain within-group Gram matrix out of distance matrix

Gram matrix Let $\bf X$ be a n x p dataset with columns (variables) centered. Then p x p $\bf X'X$ is the total scatter matrix ...
183 views

### Common methods to calculate total distance from data with categorical, continuous and counting variables

I have a data set with categorical, continuous and counting variables. I want to be able to use a method that will give me a distance for each pairwise data point. From my understanding, each type ...
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### Which dimensionality reduction technique preserves the k nearest neighbors (euclidean space)?

I'm looking for a lower dimensional projection of data such that the k nearest neighbors (in Euclidean space) in high dimensions remain the k nearest neighbors in low dimensions. I found that Isomap ...
136 views

### Normalized weighted Euclidian Distance

Hi Guys I would like to get an euclidian distance and be able to compare multiple set o data but I am not sure how to normalized it properly Each data set have values ranging from (-100->100) and ...
541 views

### Distance Matrix for Big Data

I have been struggling to create a distance matrix for some Big Data (800,000x20). I have tried R (dist function), Matlab (pdist function), and cloud computing (to increase RAM). Ultimately, the ...
719 views

### Distance between all the dots in a scatterplot in R [closed]

Is there any possibility in R to get a list of distances between all the points in a Scatterplot? For instance, this Scatterplot: So, I would like to obtain a list of distances among all the points.
3k views

### Explanation for MSE formula for vector comparison with “Euclidean distance”?

I study the loss functions for regression tasks with artificial neural networks. In case of evaluating loss with Mean Squared Error for multidimensional outputs I read the following usual formula ...
199 views

### Analysis or Comparison of Euclidean Distance matrix

Related: Average distance in distance matrix I'm looking for some way to compare euclidean distance matrices. The matrices I need to compare will have constant number of rows but varying number of ...
168 views

### Mathematical term for finding the variance of the euclidean distance between two matrices of the same dimension?

Is there any mathematical term for carrying out this procedure. I am finding the euclidean distance between the rows of the matrices by treating them as individual vector. Then I get a single vector A ...
371 views

### Why is only Euclidean distance allowed to be used for Ward's method? [duplicate]

Using scipy, I noticed that I am allowed to use only Euclidean distance for Ward's method. Is it because Ward's uses Error Sum of Squared? What if I use Ward's method with cosine similarity? Cosine ...
37 views

### Measuring simmilarity of observations (non numeric)

I have a dataset of format : day,measurement1,measurement2 1,a,b 1,a,c 1,f,s 2,a,b 2,a,c 2,f,g 3,a,d 3,a,q 3,f,s In this example day1 is more similar with day2 ...
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### Dummy data clustering: OK to apply Euclidian distance hierarchical clustering on Jaccard similarity matrix?

I created dummy variables (binary data) from categorical variables where I want to partition N subjects into multiple classes by some clustering method. I created a Jaccard similarity index matrix for ...
15k views

### Cosine Distance as Similarity Measure in KMeans [duplicate]

I am currently solving a problem where I have to use Cosine distance as the similarity measure for k-means clustering. However, the standard k-means clustering package (from Sklearn package) uses ...
21 views

### Question related to symmetry in distances

My dataset represents products and evaluation of every product by users. E.g., I might have: ...
381 views

### linear decision boundaries of using the Euclidean distance?

How does Euclidean-norm in K-means cause it to have linear decision boundaries? The Euclidean-norm is a non-linear measure between two points, then how does it make the boundaries till linear?
92 views

### Is the Normalized Information Distance (NID) a euclidean distance?

I'm trying to determine if I can plot my dissimilarity matrix (which is a Normalized Information Distance / the "Universal Distance Metric" dissimilarity matrix), in a lower-dimensional space using ...