All Questions
31 questions
13
votes
3
answers
49k
views
An example where the output of the k-medoid algorithm is different than the output of the k-means algorithm
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.
- 623
10
votes
2
answers
25k
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.
- 113
5
votes
1
answer
1k
views
Can silhouette be calculated with distances to centroids, 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 ...
- 270
5
votes
1
answer
2k
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 ...
- 1,329
4
votes
1
answer
717
views
Why use K-medoids for sequence analysis?
In the package WeightedCluster there seems to be facilities for using K-medoids clustering (i.e. wcKMedoids()), but not the more ...
- 3,063
4
votes
3
answers
4k
views
Clustering based on large Jensen-Shannon Divergence distance matrix
I have a dataset with large number of features and about 15 000 observations. I’m using a probability distribution distance metric related to Jensen-Shannon divergence (JSD) to cluster the ...
- 151
4
votes
1
answer
2k
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 ...
- 2,877
4
votes
1
answer
431
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 ...
- 41
4
votes
0
answers
30k
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 ...
- 41
3
votes
1
answer
2k
views
Assign new data to a cluster (using Gower distance and PAM algorithm)
I have a dataset which has mixed data types and hence I used Gower dissimilarity matrix as input to cluster the data using Partitioning Around Medoids (PAM) algorithm.
I wanted to know if there is any ...
- 63
3
votes
2
answers
353
views
SSE for K-means and K-medoids
I am trying to understand given same data set and same K - will the SSE of K means be higher than K Medoids or not.
both try to minimize the SSE and K-medoids is more robust to outliers - does it mean ...
- 31
3
votes
0
answers
410
views
When to use K-Medoids instead of K-means
When it's better to use K-Medoids rather than K-Means? Can anybody give some examples of dataset for the same?
- 51
2
votes
1
answer
61
views
Covering 2D data by m squares (alternative to k-means)
Let us have some data $x_i\in\mathbb{R}^2$ for $i=1,\dots,n$. Let $m=1000$. Let a small number is given, e.g. $m=5$.
The goals is to cover $n$ data by $m$ squares of the same size. The size shall be ...
- 2,846
2
votes
2
answers
2k
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 ...
- 23
2
votes
1
answer
1k
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 ...
- 2,877
2
votes
1
answer
244
views
Spectral clustering using techniques other than k-means?
In Spectral Clustering, the algorithm suggests performing K-means to $k$ eigenvectors of the resulted Laplacian matrix.
Can I use other clustering algorithms such as k-medoids or other non-distance ...
- 263
1
vote
1
answer
412
views
Clustering issues
I have a list of electrical feeders. I want to cluster them by their topological characteristics: voltage level, total length, % of underground cable, state of the neutral.
I first made a manual ...
- 21
1
vote
1
answer
1k
views
Compare clustering results based on intra cluster similarity
I am working on a project for my university. A part of this project is to compare the influence of PCA on clustering. Therefore I have a football player dataset that contains a feature called "...
- 205
1
vote
1
answer
113
views
Understanding PAM - why is it greedy?
I've been studying k-medoids for a while but i can't understand the first step or BUILD step: in particular i can't get how the initial medoids would be "greedy". I'm not much confident with the ...
- 53
1
vote
1
answer
3k
views
Silhouette value after normalizing a variable and re-clustering
I have a non-normalized variable and other normalized variables and I make a clustering with k medoids (or k means).
If I let the first variable non-normalized, I get better results in terms of ...
- 21
1
vote
0
answers
318
views
Deterministic Methods to Initialize K-Means and K-Medoids Clustering Methods
I am looking for effective and deterministic methods to initialize K-Means and K-Medoids algorithms.
There is a great answer in Methods of initializing K-Means Clustering yet most of them has some ...
- 1,157
1
vote
0
answers
107
views
Graph clustering for balanced sum of absolute deviations within each cluster (same sum of intracluster distances)
I'm given a set of points and a distance matrix. With these I'm trying to develop an algorithm similar to k-means that tries to minimize the sum of distances from each cluster datapoint to it's center ...
- 133
1
vote
0
answers
2k
views
Difference between Medoid and Centroid [duplicate]
For a certain dataset, I am finding the k closest points in my dataset to the centroid of the dataset. Are these k points the k medoids or are medoids something completely different. If so, is there ...
- 11
1
vote
0
answers
295
views
More consistent medoids from Lloyd's algorithm?
I wrote an implementation of Lloyd's algorithm in Python and was running some tests. My data set is 1D (specifically dealing ...
- 111
1
vote
0
answers
358
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 (...
- 51
0
votes
1
answer
177
views
Transformation of features in KMeans when maximums and minimums are different?
I have some questions about KMeans that I would like to discuss.
I have several features, the minimum and maximum values between columns vary, so I applied the "MinMaxScaler" ...
- 101
0
votes
2
answers
1k
views
Clustering Categorical Data
I want to cluster a data set where all variables are categorical.
Which would be more effective for doing so, k - means or k - medoids?
The data set is linked below.
https://archive.ics.uci.edu/...
- 63
0
votes
1
answer
2k
views
How to select a single updated centroid if multiple centroids are equidistant for a single group when running k-means/k-medoids?
I am trying to write my own k-means and k-medoids clustering algorithms. I understand the general idea: given k centroids, one continually updates the centroids ...
user223366
0
votes
0
answers
245
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 ...
- 2,877
0
votes
0
answers
17
views
Bisecting K-mediods [duplicate]
Is there an algorithm like Bisecting K-mediods and what would its advantages/weaknesses be?
It seems to me that it could be used well in combination of Dynamic Time Warping for clustering time series....
- 2,877
-1
votes
2
answers
647
views
how to classify input image using clustering algorithm such as k-mean?
I want to classify cifar10 images using a clustering algorithm (k-mean). Each image in the cifar10 dataset has a label, so, the results must be a set of labels which are corresponding to the test ...
- 115