k-means is a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.

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How ksvd algorithm is considered generalized kmean?

I am trying to understand more details of this paper "KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation" proposed a new algorithm called Ksvd and claims it's a ...
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2answers
85 views

X-means algorithm and BIC

I want to simulate X-means algorithm based on [1] in MATLAB. I have some questions about this algorithm. X-means Algorithm Steps: (1) Initialize K = Kmin. (2) Run K-means algorithm. (3) FOR k = ...
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1answer
33 views

k means clustering on sales geolocation data

I have geolocation data (lat and long) per customer per online purchase, and my end goal is to identify common locations per purchase per customer. (basically to see what people typically buy when ...
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0answers
99 views

R: silhouette with k-means

I'm currently checking some clustering evaluation indexes in R, and now I'm using Silhouette and its respective function in R, "silhouette" (in "cluster" package). To test the method, I used the ...
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1answer
18 views

G-Means: Learning K in K Means

I am currently trying to understand the paper 'Learning K in K-Means' by Hamerly & Elkan (2004), which is the paper implementing G-Means, an automated way in selecting K for K-Means. There is one ...
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1answer
14 views

Data transformations and regression/k-means assumptions

I have a set of independent variables and one dependent variable. I am performing regression analysis and k-means with those variables and I am wondering the following: 1) After reading this ...
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1answer
63 views

Understanding k-means unsupervised learning for features

I'm following this paper: http://ai.stanford.edu/~ang/papers/icdar01-TextRecognitionUnsupervisedFeatureLearning.pdf And I'm trying to understand specifically how the k-means approach works when ...
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3answers
135 views

How do I determine whether my data is spherically separable?

Is there a simple statistical test that I can use to determine whether my data is spherically separable? I am planning to use Kmeans++ to divide 48 dimensional vectors into clusters but I just read ...
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20 views

K means algorithm on an image

Hi I want apply my k means algorithm on an image to a certain number of k clusters.I want to ask is that How is the algorithm applied ? I mean what i have learned from internet is that k means is ...
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1answer
16 views

Clustering when similarity/affinity matrix is binary

I have n articles and a list of articles for each article implying similarity, e.g., a1 -> a3, a5 means ...
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37 views

What could cause a K-means clustering algorithm to converge into a single cluster?

I am currently writing a K-means clustering algorithm in Python, and I seem to have coded myself into a corner... I begin my algorithm with data sets assigned randomly to the appropriate number of K ...
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2answers
59 views

Find the optimal number of clusters in large dataset using R

I've a got a data which I did a PCA on. I want to do a kmeans on the individuals coordinates on the 5 first principal components. Therefore I have a 200000 x 5 matrix of the coordinates. I'm looking ...
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20 views

Clustering 2-Dimensional Data (Y,X) with Forced Linear Decision Boundaries Having Positive Slope and Intercept

I have 2-Dimensional Data (Y,X) on which some clustering method needs to be applied. The decision boundary between clusters must follow some linear equation Y=aX+b because the slope a of the linear ...
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1answer
28 views

Can silhouette be calculated with centroid distances, instead of pairwise point distances?

I am using Silhouette cluster validation for each repetition (for a specific K) of k-means, k-modes and k-medoids. All the definitions of Silhouette I see calculate the distance of each point to ...
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1answer
28 views

Outliers detection for clustering methods

I'm in the middle of a result analysis for some clustering methods, doing quality tests for different clustering outputs coming from a singular input dataset where data preprocessing and cleaning ...
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35 views

X-Means Likelihood for BIC

I have recently been trying to understand the X-means method for deciding on K, using BIC. However I have become stuck on one particular equation in the original paper. On the 4th page, when ...
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2answers
29 views

Grouping customers together into like groups based on multiple variables without a categorical variable

I am looking for a little guidance as to the correct approach to this problem. We have a list of IDs and roughly 8 different numerical variables such as quantity and revenue. Each ID is unique to the ...
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58 views

Using BIC,AIC for estimating number of clusters in document clustering using Kmeans

In my approach I am trying to find the optimal value of 'k' for clustering a set of documents using KMEANS algorithm. I wanted to use 'AIC' and 'BIC' information criterion function for finding the ...
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59 views

How should one learn the centers for an hyper basis function network (HBF)?

I was reading the following paper on hyper basis function (HBF) (similar to radial basis function RBF network) and was trying to figure out how one learns the movable centers of the hyper basis ...
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1answer
33 views

Traffic Analytics using k-means

I'm going to provide some (near)real time analytics (classification) of the network traffic inside of my cluster. All traffic is aggregated into "session" and consists of some number of features. I've ...
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1answer
27 views

Clustering Analysis for large data in R

I am trying to perform a clustering analysis for a csv file with 50k+ rows, 10 columns. I tried k-mean, hierarchical and model based clustering methods. Only k-mean works because of the large data ...
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2answers
29 views

Clustering Analysis for large data in R

I am trying to perform a clustering analysis for a csv file with 50k+ rows, 10 columns. I tried k-mean, hierarchical and model based clustering methods. Only k-mean works because of the large data ...
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2answers
61 views

How to predict property value using lat/lon?

I have lat/lon and property values for households in a particular region. Format: Lat Lon value 32.2 -98.22 120000 .... Now I have new data of the ...
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2answers
43 views

Silhouette clustering index in practice

I don't have much experience with data analysis algorithms (data mining, machine learning, if you like) and I'm interested if some could share their experience with practical usage of Silhouette in ...
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1answer
46 views

K mean clustering algorithm on 1D data

I'm really confused on what are the steps on how to perform k-means clustering algorithm on 1 dimension data. So suppose I have the following array of data and it should be clustered in two groups: ...
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30 views

K-means cluster discrimination in R

I've run a k-means cluster analysis in R and have identified 6 unique clusters. When I assign the cluster number back to the raw data, I see that there are overlaps of variables for certain clusters. ...
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49 views

Checking the assumptions of K-means clustering

I want to do a k-means clustering on a dataset containing 22 numerical variables between 0 and 100 and 75 observations using R. I read this post How to understand the drawbacks of K-means on k-means ...
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1answer
36 views

Change in r squared due to clustering in multiple linear regression

Puny undergraduate stats student here. I am examining the effect of two regressors on a predictor. OLS on the raw data (approx 200k cases) yields next to no correlation in the following models: ...
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19 views

Robust Sparse K Means clusters and valid index

I used the robust sparse k-means for clustering my dataset and I would like to calculate some distance-based statistics for evaluating my results. Should I compute them on the dissimilarity matrix ...
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1answer
26 views

Functional clustering with R [closed]

I have a time series data in R, and I am using functional clustering. I would like to interpret a figure that is output below the code. Furthermore, I would like to control line colors and thickness ...
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2answers
58 views

K-means - comparing solutions with SSwithin elbow-method: minimum “too early”

I am running a k-means clustering process in R and I'm comparing cluster partitions of different number of clusters: k = from 1 to 17. Using the elbow-method, I have a minimum at k=5, but this value ...
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1answer
82 views

Interpret the visualization of k-mean clusters

Following my posted data here, I conducted a k-mean clustering analysis. I refereed to this post: How to produce a pretty plot of the results of k-means cluster analysis? for the clusters ...
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4answers
338 views

How I can convert distance (Euclidean) to similarity score

I am using $k$ means clustering to cluster speaker voices. When I compare an utterance with clustered speaker data I get (Euclidean distance-based) average distortion. This distance can be in range of ...
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2answers
146 views

K-means: Why minimizing WCSS is maximizing Distance between clusters?

From a conceptual and algorithmic standpoint, I understand how K-means works. However, from a mathematical standpoint, I don't understand why minimizing the WCSS (within-cluster sums of squares) will ...
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51 views

kMeans unsupervised feature learning on multiple layers

I'm trying to develop an unsupervised feature learning pipeline. I have a train set with 512x512 images. I've extracted 16x16 patches, performed preprocessing steps (normalization and whitening). ...
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55 views

Dig deeper on “Determine the Number of Clusters and Validate It”

Updates to this thread: Based on Anony-Mousse's comments on my current results, there is only one big cluster in my data set. However, I think it might still be possible to reveal the clusters if I ...
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1answer
49 views

Incorporating new words in tfidf feature-vector for online clustering

I am building an Online news clustering system using Lucene and Mahout libraries in java. I intend to use vector space model and tfidf weights for Kmeans(or fuzzy/streamKmeans). My plan is : Cluster ...
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35 views

X-Means Calculation of BIC

I am trying to calculate the BIC for the X-Means algorithm as described in the paper by Pelleg and Moore (https://www.cs.cmu.edu/~dpelleg/download/xmeans.pdf). The paper describes the calculation of ...
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1answer
148 views

How PCA would help the K-mean clustering analysis?

Background: I want to classify the residential areas of a city into groups based on their social-economic characteristics, including housing unit density, population density, green space area, housing ...
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2answers
47 views

Finding cluster number based on distance & max element count

Given two constraints: The maximum distance d an element can lie from a cluster centroid (or medoid) The maximum number of elements n in one cluster Is it possible to find the minimum number of ...
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1answer
288 views

difference between k means and k medoid

I understand the difference between k medoid and k means. But can you give me an example with a small data set where the k medoid output is different from k means output
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1answer
25 views

Clustering matrices with “2d interpretation”

I am not sure if I can formulate this such that it is clear. :) I have around 700 80x80 matrices, where each matrix shows some weather event (a matrix has continuous entries from 0 to 60). Now I ...
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24 views

Streaming K-medoids

Mahout, Hadoop machine learning library, contains an implementation of Streaming K-means algorithm that is based on the following paperworks The Effectiveness of Lloyd-Type Methods for the k-Means ...
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2answers
69 views

Performing k-means clustering on a set of lines

I have a set of lines (y = numbers between 1 and 100, x= discrete) that I am trying to cluster to group similarly-shaped profiles. I have found that the profiles seem to cluster the cleanest when ...
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44 views

How to compare clustering algorithms of numerical and nominal data

I have a dataset for clustering including numerical and nominal variables. I would like to compare the k-means and k-medoids clustering algorithms and I would also like to find the optimal k-value ...
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68 views

Trying to understand xmeans (using R, RWeka)

In a project I want to use XMeans to estimate the 'optimal' number of clusters that are distinguishable in different datasets. The numbers I got seemed too low, so I experimented a bit with generated ...
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2answers
368 views

Do I need to remove duplicates for cluster analysis?

I am doing a cluster analyis and I was wondering whether it is possible to remove duplicates from the data set - in order to increase performance. I work on tables where objects are in rows and ...
3
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1answer
51 views

what's the implementation of SciKit-Learn K-Means for empty clusters?

SciKit-Learn's K-Means doesn't discard empty clusters (code of particular function here). Instead, it looks for the pattern that is furthest away from its assigned centroid (assigned cluster but I ...
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1answer
72 views

Difference between PCA and spectral clustering for a small sample set of Boolean features

I have a dataset of 50 samples. Each sample is composed of 11 (possibly correlated) Boolean features. I would like to some how visualize these samples on a 2D plot and examine if there are ...
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46 views

Feature selection in clustering

I am looking for a method for feature selection in Gaussian Mixture Models. I have a dataset with 2000 records and 40 variables. I tried to use the "clustvarsel" package in R, which use the BIC as ...