-1
votes
1answer
36 views

k-Means Clustering vs. Hierarchical Clustering

Can you please provide One advantage of "k-Means" compared to "Hierarchical Clustering" One advantage of "Hierarchical Clustering" compared to "k-Means" Thanks in advance !!
0
votes
1answer
58 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
33 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
34 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
16 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
33 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
28 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
30 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
28 views

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

consider the following example data: ...
2
votes
1answer
22 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
22 views

How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
1
vote
0answers
27 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
33 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
67 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
68 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
35 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
60 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 ...
3
votes
1answer
33 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
59 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
120 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
42 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
28 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 ...
0
votes
3answers
540 views

Why does k -means clustering algorithm use only euclidean distance metric?

Is there a specific purpose in terms of efficiency, functionality why k-means algorithm do not use cosine similarity as a distance metric and use the euclidean norm
6
votes
2answers
133 views

Why only the mean value is used in (K-means) clustering method?

In clustering methods such as K-means, the euclidean distance is the metric to use. As a result, we only calculate the mean values within each cluster. And then adjustments are made on the elements ...
1
vote
1answer
33 views

How many different random initializations should I perform with Lloyd's algorithm to obtain the optimal clustering with X% of confidence?

I use Lloyd's algorithm for clustering. Since it relies on a random initialization and Lloyd's algorithm can get stuck in local optima of the k-means objective function, I have to run it several ...
2
votes
1answer
87 views

Validity Index Pseudo F for K-Means Clustering

The Validity Index "Pseudo F" is described as: (between-cluster-sum-of-squares / (c-1)) / (within-cluster-sum-of-squares / (n-c)) with c beeing the number of clusters and n beeing the number of ...
5
votes
1answer
221 views

Clustering inertia formula in scikit learn

I would like to code a kmeans clustering in python using pandas and scikit learn. In order to select the good k, I would like to code the Gap Statistic from Tibshirani and al 2001 (pdf). I would like ...
1
vote
0answers
23 views
2
votes
1answer
109 views

Estimating most important features in a k-means cluster partition

Is there a way to determine which features/variables of the dataset are the most important/dominant within a kmeans cluster solution generated via R?
2
votes
2answers
152 views

Clustering with K-Means and EM: how are they related?

I have studied algorithms for clustering data (unsupervised learning): EM, and k-means. I keep reading the following : k-means is a variant of EM, with the assumptions that clusters are ...
2
votes
2answers
205 views

How to determine which method is the most reasonable clustering results?

Method 1: Cluster by K-means with initial centroid {27, 67.5} Method 2: Cluster by K-means with initial centroid {22.5, 60} Method3: Agglomerative Clustering How can I know which method ...
1
vote
1answer
81 views

Should I standardize my variables for this particular case of cluster analysis?

I'm trying to cluster a list of records based on a (percentage) frequency distribution of variables which add up to 100%. Like Record1 - VarA(25%) VarB(25%) varC(50%) varD(0%) Record2- VarA(50%) ...
0
votes
0answers
439 views

Calculate BIC to determine the optimal number of clusters (k-means clustering)

I have a set of data and want to know whether they fall in 1, 2 or 3 groups. I started exploring the question by using k-means in MATLAB. By just looking at the distance from the centroid of each ...
2
votes
1answer
124 views

Silhouette coefficients after deleting some data and re-clustering

I was working on a dataset today on which I used K-means clustering algorithm and then calculated the Silhouette coefficients for each point. I then removed 5% of the data with worst silhouette ...
1
vote
1answer
74 views

Cluster large boolean dataset

I got a dataset with about 5,000 columns and about 135,000 rows - all fields contain boolean (binary) data. I am looking to classify each of these columns into one of 50 groups, based on similarity. ...
1
vote
2answers
822 views

K-Means Clustering - Calculating Euclidean distances in a multiple variable dataset

I have just completed a simple exercise with 2 variables (X and Y) to understand how K-Means clustering works. The results look like this, My questions is, if I have another column Z, how should ...
1
vote
1answer
148 views

K-means clustering for usage profiling

I am trying to use k-means clustering to profile mobile device usage behaviour for IT users. My data consists of different system and user level variable/readings like number of calls/sms, cpu/memory ...
0
votes
1answer
121 views

Representative point of a cluster with L1 distance

The representative point of a cluster or cluster center for the k-means algorithm is the component-wise mean of the points in its cluster. The mean is chosen because it helps to minimize the within ...
1
vote
1answer
157 views

What is the initial partition for k-means in R?

My question is probably elementary, and I apologize for that. I am reading Kogan's "Introduction to Clustering Large and High-Dimensional Data"; I am interested in understanding batch K-means and ...
1
vote
1answer
225 views

Partitioning Around Medoids

I have a question regarding Partitioning Around Medoids (PAM) clustering algorithm, because everywhere I look, it is described differently. In every step of the algorithms do I swap only one medoid or ...
2
votes
1answer
76 views

Kmeans algorithm cyclical solution

I am currently implementing a Kmeans clustering algorithm in R. I am not using any packages and I wrote it from scratch. I am using only one set of initial guesses, and my action upon finding an empty ...
4
votes
2answers
126 views

What distance method to use in this scenario?

I have a 10 dimensional space which contain points that contain a 1 or 0 . example of two points : point1 : 1,1,1,0,0,0,1,1,0,1 point2 : 1,0,1,0,0,0,1,0,0,0 ...
2
votes
1answer
62 views

Find k in k-means, but only between two options

There are many algorithms for finding $k$ in the $k$-means algorithm that depend on finding the "kink" in a graph of the objective function. What if I know that the data have either $2$ or $3$ ...
2
votes
1answer
261 views

In cluster analysis, can you use Gower's coefficient of similarity with a k-means clustering method?

I am researching cluster analysis, and I am interested in variables that are both categorical and continuous, for which I have read that a Gower's similarity coefficient is a good proximity measure. I ...
1
vote
1answer
148 views

How to re-cluster new instance in centroid base clustering?

I have applied clustering algorithms like k-mean, k-medoid and DBSCAN on my patients dataset. For each algorithm RapidMiner generated a clustered model (centroid table and graphs etc) and a clustered ...
4
votes
1answer
974 views

Difference between standard and spherical k-means algorithms

I would like to understand, what is the major implementation difference between standard and spherical k-means clustering algorithms. In each step, k-means computes distances between element vectors ...
0
votes
0answers
102 views

Hierarchical clustering output in spss to determine no of clusters?

I have applied hierarchical (agglomerative) clustering in SPSS on my 100 records dataset. The rule says that 'where the distance coefficients makes the larger jumb that point determines the no. of ...
2
votes
2answers
116 views

K-means as limit of Soft K-means algorithm

I encountered the following exercise Show that as the stiffness $\beta$ goes to $\infty$, the soft $K$-means algorithm becomes identical to the original hard $K$-means algorithm, except for the ...
0
votes
0answers
80 views

Clustering Longitudinal Data with kml in R

I have data of probability values which vary over time: ...
1
vote
2answers
125 views

Similarity between objects based on tags (binary features)

I have five millions of objects each of them having one or more tags. How do I compute statistically sound similarity score between each pair of the objects taking into account that: There are 100 ...