0
votes
2answers
27 views

Kmeans cluster size change quite a bit on each run

I am running a kmeans on a sample size of 1000 data. The data is scaled (z). When I run kmeans(df, nstart=25, centers=5)- it runs and I can get the size of each cluster. The largest group has 620 in ...
1
vote
0answers
17 views

Kmeans plotting on discriminant components

When you plot a kmeans model (in R) with the plotcluster() function, it plots the clusters against the axis of the 1st and 2nd discriminant components (dc). In reading about these axis- some state ...
-1
votes
0answers
16 views

Clustering using different distance measures [duplicate]

I do unsupervised clustering for a dataset using k-means algorithm. I want to know what is the difference between different distance measures (Euclidean, cityblock, cosine and correlation,...etc). I ...
1
vote
2answers
28 views

How to compare two clusterings generated by two clustering approaches

I am currently working on a modification of a clustering algorithm to suit my problem domain. I want to know which methods are available for me to compare the centroids generated from the two ...
4
votes
2answers
81 views

Using k-means with other metrics

So I realize this has been asked before: e.g. What are the use cases related to cluster analysis of different distance metrics? but I've found the answers somewhat contradictory to what is suggested ...
0
votes
2answers
46 views

What's a good way to mentally visualize n dimensions in a k means

I've been using k-means to do some clustering and one of the ideas I'm struggling with is the n dimensions aspect. If I were clustering housing prices vs sq. feet its just a simple 2d graph. That I ...
0
votes
0answers
23 views

How to measure the similarity of k-means clustering using different datasets?

I run k-means clustering on my dataset (100 samples in total) and partition the data into k=5 clusters. Then I want to test how robust of the k-means can be; however, I haven't got more new data ...
0
votes
0answers
23 views

How can I evaluate the accuracy of a clustering when I don't have information on the true class labels?

Already classified data set for the t-shirt factory problem I want to calculate the accuracy of my algorithm. I have the training data without any size information and I couldn't find the classified ...
0
votes
1answer
34 views

Spherical K-means Clustering in R

I have a large data set that I would like to cluster using spherical K means algorithm. However, I am relatively new to this subject and R in general. Most of my knowledge is self taught and I am ...
0
votes
0answers
50 views

I find very different results using a k-means or two-step clustering method. How is this?

I want to use a cluster analysis (CA) in SPSS to define different profiles in my dataset. I am using different continuous variables for this, including several neuropsychiatric measures. I am new in ...
0
votes
2answers
81 views

k-means vs k-median?

I know there is k-means clustering algorithm and k-median. One that uses the mean as the center of the cluster and the other uses the median. My question is: when/where to use which?
4
votes
3answers
137 views

K-means cluster analysis with K=2 as a binary classifier

I used two variables, height and weight, and using K-means cluster analysis with $K=2$, two clusters were obtained. I used $K=2$, as the observations either belong to men or women. I then compared the ...
1
vote
1answer
35 views

A clustering and classification question

I'm trying to classify my set of data into two classes (introvert / extrovert). I was thinking of using a decision tree at first, but I don't have any potential known results in order to create my ...
0
votes
1answer
31 views

Classifying a set of photos to places

I want to cluster photos and map them to places. As input I have Photos with locations (lat, long) Places - some as (imprecise) bounding boxes, some just as points, maybe others as bounding ...
1
vote
2answers
49 views

clustering accuracy

I have a general doubt regarding clustering. I have a data set of size 1196*18675. where 1196 is the no of documents. I am trying to cluster the data with k=7 using k-means. Each time the clustered ...
0
votes
0answers
15 views

Turning MiniBatchKMeans into Fuzzy MiniBatchKMeans

I'm using Scikit-Learn, which has an implementation of MiniBatchKMeans. I'm very inexperienced with ML, so I'm wondering how (if ...
2
votes
1answer
41 views

Which clustering technique to use for a temporal dataset?

I have seen a similar question but thought I'd ask my own to hopefully garner some usefull feedback. Basically, I have a large temporal dataset, consisting of domestic smart energy meter use ...
1
vote
1answer
34 views

Assigning meaningful cluster name automatically

The objective of my work is to cluster the text documents. Once the documents are clustered, traditionally the system will assign numeric value for the clustered group. For example if I have 5 ...
1
vote
1answer
55 views

K-means cluster Analysis and 4-point Likert Scales

Is there a concern using a 4-point likert-type scale (i.e., agreement) when attempting a cluster analysis using k-means clustering? Most of the data for the items in my data set are favorable (e.g., ...
3
votes
2answers
93 views

Feature / attribute selection for k-means or other clustering

It seems to me that in literature it is assumed that one knows which features / attributes to choose to characterize an item in clustering. If I have a database with items which have many attributes, ...
1
vote
1answer
85 views

Clustering algorithms for extremely sparse data

I am trying to cluster an extremely sparse text corpus, and I know the number of clusters (my data is the title and author list of scientific publications, for which I already know the number of ...
1
vote
0answers
59 views

Clustering on a data set with mixed variables

I have a data set consisting of $n$ elements with $d$ features for each element ($x_{i,f}$ means the value for the f-th feature of the i-th element). I would like to cluster this data set into $k$ ...
1
vote
0answers
22 views

ML Bank transactions assignement to invoices

In a effort to reduce human intervention, I'm trying to optimize the process of assigning bank transactions to invoices. This task should be done once every year, so we can assume our dataset won't ...
7
votes
3answers
378 views

Clusterings that can be caused by K-means

I have gotten the following question as a test question for my exam and I simply cannot understand the answer. A scatter plot of the data projected onto the first two principal components is shown ...
0
votes
0answers
36 views

How to test the significance of clusters?

How can one test the significance of the clusters obtained after a clustering procedure? Are there separate tests for the distance/similarity/dissimilarity measure used to get the distance matrix and ...
2
votes
1answer
54 views

Intuition behind the Calinski-Harabasz Index

Given $CH(k) = [B(k) / W(k) ] \times [(n-k)/(k-1)]$, where $n$ = # data points $k$ = # clusters $W(k)$ = within cluster variation $B(k)$ = between cluster variation. It is my understanding that the ...
0
votes
1answer
76 views

Interpretation of NbClust result

The plots show the output of NbClust(). By looking at the plot, is that correct to say that k=5 is the optimal number of ...
3
votes
1answer
123 views

Bayesian Networks and discretization of variables using K-means clustering

In many approaches to learning Bayesian Networks a solution to tackle continuous variables is to discretize them and apply one of the well established techniques for learning Bayesian Networks ...
0
votes
0answers
19 views

Clustering groups that have replicated measures: hierarchical clustering on group-average VS regression tree

I measured 2 continous dependent variables (V1 and V2) on 10 occasions (10 replicates) for each of 4 groups. I aim to cluster my groups. i.e. I dont want to cluster replicates, since this could mix ...
0
votes
1answer
235 views

usefulness of k-means clustering on high dimensional data [duplicate]

I wonder what is the usefulness of k-means clustering in high dimensional spaces, and why it can be better (or not) than other clustering methods when dealing with high dimensional spaces.
0
votes
2answers
119 views

Streaming k-means

I want to perform something like streaming/online/out-of-core kmeans clustering on large data. Here is simple idea: Break all data into N chunks. Read from disk 1st chunk and calculate centroids ...
1
vote
1answer
62 views

Alternative to spherical K-Means for clustering large high dimensional dataset

What are some alternatives to Spherical K-Means for clustering very large datasets of high dimension? I'm looking for something that will be fast even on large datasets, and preferably will not ...
1
vote
0answers
26 views

Clustering Techniques

I'm a little new to data mining and would definitely appreciate some tips. I'm using clustering algorithms looking for possible grouping in some variables described below. I've been using the Excel ...
1
vote
0answers
123 views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
1
vote
0answers
47 views

Vector Quantization of heavy tailed distribution

I'm generating with Monte Carlo simulation some stock price $X$. Once I have the stock price sample, I want to cluster it with 100 points $\hat{X}$. My problem is that the error associate with my ...
0
votes
1answer
63 views

Can component scores be used for further analyses, e.g. cluster analysis?

I have done a principal component analysis using SPSS and now have 3 components. 2 components have 4 items in the subscale, and 1 component has 3 items. Component scores using regression for each ...
1
vote
0answers
45 views

Advice on how to analyse “customer-data” in R

consider the following example data: ...
2
votes
1answer
65 views

Finding the cluster centers in kernel k-means clustering

I think this is the most easily understood topic in Kernel K Means Clustering. But assuming that I am not an expert in Machine Learning, can someone tell me how does someone calculate Kernel K means ...
0
votes
0answers
45 views

How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
1
vote
0answers
39 views

Exact derivation for finding k-means from Gaussian Mixtures

I am having difficulty in deriving k-means from Mixture of Gaussians. I am following the notation from Bishop (2006), Section 9.3.2: Suppose we have : $$ p(\mathbf{x}| \boldsymbol{\mu}_k, ...
1
vote
1answer
49 views

What do you do when a centroid doesn't attract any points?

I am solving a clustering problem on a trivial dataset with the k-means algorithm. I am running a couple of tests and, from time to time, one centroid doesn't attract any points, i.e. I've got an ...
1
vote
3answers
276 views

Is it important to scale data before clustering?

I found this tutorial, which suggests that you should run the scale function on features before clustering (I believe that it converts data to z-scores). I'm wondering whether that is necessary. I'm ...
5
votes
2answers
101 views

What do you do when there's no elbow point for kmeans clustering

I've learned that when choosing a number of clusters, you should look for an elbow point for different values of K. I've plotted the values of withinss for values of k from 1 to 10, but I'm not seeing ...
1
vote
1answer
98 views

K-medians, formula to compute the median

If you are running K-medians, and your distance metric is the L1 norm, how do you derive that the center of each centroid is the median of the data points assigned to it? Second, how do you compute ...
4
votes
1answer
87 views

What algorithm should I use to cluster a huge binary dataset into few categories?

I have a large (650K rows * 62 columns) matrix of binary data (0-1 entries only). The matrix is mostly sparse: about 8% is filled. I would like to cluster it into 5 groups - say named from 1 to 5. I ...
4
votes
1answer
61 views

gaussian mixture model - approximate a matrix

I have a similarity matrix M - the value M(i,j) indicates the similarity between two elements i and j. I want to approximate that matrix using a Gaussian Mixture model or I want to cluster that ...
0
votes
2answers
140 views

Is clustering (kmeans) appropriate for partitioning a one-dimensional array?

I want to group the outcome of a function into 2 (or 3) categories. I have a function efficiency=f(weight,speed,#refueling_stops) that takes 3 input parameters and the output tells me how "efficient" ...
3
votes
2answers
424 views

Cluster analysis on panel data

I have a panel data set (country and year) on which I would like to run a cluster analysis by country. My data set has around 20 variables. Here's a summary for my panel data: ...
-2
votes
1answer
59 views

k-medoids algorithm with incomplete distance matrix

I want to apply k-medoids algorithm using an incomplete distance matrix as input. How can I handle the lack of information of this matrix? Just ignoring the missing distances? Or is there a better ...
1
vote
1answer
54 views

Interpreting standardized mean centers in a cluster

I created a $k$-means with 3 clusters. Some of the variables had a big scale, so I used a $z$-score to standardize them. The others (mostly dummies), I left as is. Now, when I create the table of all ...