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.

learn more… | top users | synonyms

0
votes
0answers
6 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 ...
0
votes
2answers
24 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 ...
0
votes
0answers
37 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 ...
1
vote
0answers
48 views
+50

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 ...
0
votes
1answer
31 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 ...
1
vote
1answer
23 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 ...
0
votes
2answers
21 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 ...
2
votes
2answers
50 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 ...
1
vote
2answers
26 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 ...
1
vote
1answer
33 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: ...
0
votes
0answers
18 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. ...
1
vote
0answers
42 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 ...
0
votes
1answer
29 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: ...
0
votes
0answers
17 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 ...
-1
votes
1answer
22 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 ...
2
votes
2answers
42 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 ...
0
votes
1answer
68 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 ...
5
votes
4answers
330 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 ...
2
votes
2answers
86 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 ...
1
vote
0answers
36 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). ...
0
votes
2answers
49 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 ...
1
vote
1answer
30 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 ...
1
vote
0answers
18 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 ...
2
votes
1answer
107 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 ...
2
votes
2answers
39 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 ...
0
votes
1answer
102 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
0
votes
1answer
23 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 ...
0
votes
0answers
20 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 ...
2
votes
2answers
63 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 ...
0
votes
0answers
35 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 ...
0
votes
0answers
43 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 ...
5
votes
2answers
359 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
votes
1answer
46 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 ...
1
vote
1answer
67 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 ...
1
vote
0answers
39 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 ...
2
votes
0answers
39 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 ...
0
votes
1answer
99 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 ...
0
votes
0answers
13 views

kmeans with images CBIR

I have an image database and I want to cluster them to K clusters useing k-means clustering but I didn't know how to start. I know that matlab has a function that do this work but for simple image, ...
0
votes
0answers
28 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 ...
0
votes
1answer
57 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% ...
-1
votes
2answers
52 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 ...
0
votes
0answers
74 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 ...
2
votes
2answers
46 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 ...
3
votes
1answer
79 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 ...
0
votes
0answers
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 ...
2
votes
0answers
107 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?
0
votes
0answers
17 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 ...
0
votes
1answer
45 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 ...
3
votes
1answer
29 views

What does cluster size mean (in context of k-means)?

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 ...
0
votes
0answers
80 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 ...