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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SKLearn Clustering: how would you cluster a LARGE database of dogs? [on hold]

Given: A VERY large dataset of dogs. columns: ID (alphanumeric) Weight (numeric) Height (numeric) Eye Color (alphabet) ... (numeric) Tongue Length (numeric) How do you find what makes these ...
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2answers
33 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
30 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
312 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
49 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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24 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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2answers
46 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
19 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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13 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
80 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
28 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
39 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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21 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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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
56 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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28 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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25 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
351 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 ...
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1answer
32 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
56 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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29 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 ...
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0answers
33 views

Anomaly Analysis (K-Means) - finding suspicious activities/operators

I am relativly new to the field of data mining and want to make a anomaly detection on transactional retail data. I want to use a simple anomaly detection (kmeans at the moment) for finding suspicious ...
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1answer
93 views

K-means clustering exam question [closed]

I have an exam on the k-means algorithm and clustering and I was wondering if anyone knows how to figure out this sample exam question. My teachers are hopeless to provide any information on how to ...
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26 views

k-means random initialization for very-large dataset, is it good enough?

I've got a question in clustering using random k-centers. I ran the k-means algorithm for 10 iteration, for some 100 rows taking 9 random initialization of centroids from the data set itself. The ...
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1answer
45 views

Cluster Analysis: effectiveness of k means results and alternative methods

I have to separate 425 observations based on certain variables numbering 32. 1)I used PCA to reduce the dimensionality of Data, which gave me 32 components out of which 5 components accounted for 75% ...
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2answers
45 views

Interpreting kmeans output

I am working on a clustering model with the kmeans() function in the package stats and I have a question about the output. My data is a sample from several tech companies and AAPL._UP is a variable ...
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47 views

k-means clustering why sum of squared errors (why k-medoids not)?

K-means clustering uses the sum of squared errors (SSE) $E = \sum\limits_{i=1}^k \sum\limits_{p \in C_i} (p-m_i)^2$ (with k clusters, C the set of objects in a cluster, m the center point of a ...
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2answers
43 views

Need a little help understanding K-means++ seeding

I have been working on a project that involves using K-means clustering for generating adaptive palettes from images. I understand the general process of K-means clustering, and I understand the ...
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1answer
77 views

Controling segmentation process in order to get usable segments

My aim is to create segments based on survey data. This in it self is quite straight forward: I use PCA to extract information from the survey answers, and then ...
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15 views

Difference between criterion of k-means?

I am watching a video on k-means clustering here https://www.youtube.com/watch?v=sLf0Z9tCTjE&index=30&list=PL3DFCC23FCE3C7EFB, in which (12:14) the professor briefly mentioned some criterion ...
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103 views

Gaussian Mixture and K-Means ?! a big challenge?

This is taken from Tom. Mitche Material as Old-Exam. I think the (2) is true and not (3). Who can verify me?
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16 views

calculating distance among unordered set partitions for k-mean clustering?

I have a dataset for which I construct unordered set partitions for each data point, e.g. {{1,2,3}{4,6}{5}} for one and {{1,3}{2,4,5}{6}} for the next. I would like to perform k-means clustering on ...
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35 views

Chosing optimal k and optimal distance-metric for k-means [duplicate]

I have a data-set with roughly 20-dimensions and millions of points which I want to cluster. The goal is to find a set of clusters which: Are as distinct as possible from each other (minimum ...
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K-Means Voronoi Issue: Size?

http://en.wikipedia.org/wiki/Cluster_analysis It states that: K-means separates data into Voronoi-cells, which assumes equal-sized clusters (not adequate here) and ...
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48 views

How to show output for K-means clustering on multi-dimensional data?

I have to implement K-means algorithm for K=10 on handwritten digits data. The data matrix is 2500 X 784,i.e there are 2500 data points each with dimensions 784 .After clustering,I have to label each ...
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1answer
85 views

What is the benefit of using Manhattan distance for K-medoid than using Euclidean distance?

Please give me the reasons. I didn't find any k-medoid example that's calculation is done using Euclidean distance. All examples are made of Manhattan distance for k-medoid.
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1answer
99 views

K-means for non-spherical (non-globular) clusters

It is said that K-means clustering "does not work well with non-globular clusters." However, is this a hard-and-fast rule - or is it that it does not often work? I have a 2-d data set (specifically ...
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7answers
346 views

Comparing k-means results to original data: how to interpret the resulting plots?

I'm running k-means on my dataset that can be found here that has 7 classes. I plotted the ggpairs for the dataset and then took k-means and plotted ggpairs again ...
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1answer
53 views

Why do final cluster centers change after applying results from past K-Means clustering (SPSS)?

I have a question regarding what happens after I apply k-means clustering centers to a new data set. Basically, I ran k-means clustering on a dataset1, saved the cluster centers, and applied it to a ...
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1answer
31 views

Statistical Classification Method to Compare PURELY Categorical Data?

I have about a half-dozen variables, each of which can have anywhere from three to ten outcomes. I have to measure the degree of separation/similarity between rows. Either we can do some sort of ...
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1answer
55 views

convert categorical data into numerical data? [closed]

I am doing project for post graduation....project is document clustering.in the project raise the problem is the text data convert into the numerical data?
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1answer
58 views

Probability for selecting centroids - K-means++

K-means++ selects centroids one by one, where each point has the chance to become next centroid with probability proportional to distance to closest centroid already selected. I implemented it like ...
2
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1answer
47 views

Cluster analysis without knowing the structure of the data set

I’m working on a task regarding cluster analysis for about half a year now, but since the fields of pattern recognition and cluster analysis are quite complex ones, I would call myself a beginner in ...
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59 views

Gaussian mixture models restriction? [closed]

I read this note that with striction on GMM with some condition this algorithm is more like to K-means: the adaptations of the Gaussian mixture models algorithms with Restrict each $\Sigma_i$ ...
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2answers
50 views

Clustering not producing even clusters

I'm using k-means clustering to processes running on machines. Dataset sample : machine name, process m1,java m2,tomcat m1,word m3,excel Build a matrix of ...
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0answers
73 views

jenks natural breaks vs k-means

I am new to this topic. As far as I know both are data clustering methods. Then my question is when is Jenks prefered over k-means? I read on this website that jenks is particularly suited for ...
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2answers
37 views

k-means nstart equivalent for EM Clustering? Report only the best solution from a large number of initializations?

In K-means clustering, you can specify an nstart=i parameter, which performs the algorithm i times (i.e. selects the initial k random centroids i times) sand reports the best answer only. If I perform ...
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1answer
66 views

K-mean clustering with unknown k

How do I perform k-mean clustering with unknown k? I also need to provide a confidence interval for k. I am thinking in the line of putting a Poisson prior on k. Does that make sense? Does there exist ...
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1answer
57 views

K-Medoids swapping inside clusters

I'm a bit confused with concept of K-medoids. It seems that original algorithm (PAM) describes that swap step should be performed by swaping only one of the medoids with one non-medoid point from ...
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1answer
46 views

K-means++ like initialization for K-medoids

Does it make sense to use initialization in K-medoids like in the case of K-means++? To be precise - is it good to select "farthest" points as initial medoids? (farthest in sense that points that are ...