Questions tagged [k-medoids]
The k-medoids tag has no usage guidance.
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Formal method to predict borders for k-medoid clustering
I've got a simple (two variable) data set that resolved nicely with k-medoids clustering (PAM) into three clusters. However, for presentation, I'd like to plot the points along with a Voronoi-style ...
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K-medoids/PAM - Dissimilarity Matrix
Just wondered if someone could provide some clarity on whether it is suitable to use partitioning around medoids (PAM) on a dataset that has not been transformed into a dissimilarity matrix?
For ...
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k-medoids on binary data
I have a binary dataset and I would like to cluster it with the k-medoids algorithm. The dataset is not huge: I have 10 dimensions and around 250 objects. I am clustering physical infrastructures ...
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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" ...
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Understanding the SWAP step of PAM K-medoids
In what I believe is the original PAM paper, the swap step is described like so:
"This is done by considering all pairs of objects (i, h) for which object i has been selected and object h has ...
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K-Medoid Clustering with Point Weights
I implemented a K-Medoid clustering algorithm recently; I have a number of points $x_1, ..., x_n$ which have various properties and a distance function $d$ that maps two points to some nonnegative ...
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Clustering a distance matrix with k-medoids
For a symmetric distance matrix that I want to cluster, I performed several cluster algorithms:
MDS into k-Means
DBSCAN
OPTICS
k-Medoids (the one I'm having trouble with)
Now, I would like to know ...
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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?
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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 ...
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K-medoids: Is there any constraint about the choice of the distance?
It is well known that the K-means algorithm is well designed for the Euclidean distance (or a minor variation such as the cosine distance). I have been reading the paper "A simple and fast algorithm ...
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Is K-medoids / partitioning around medoids (PAM) appropriate for clustering data with many zero values?
I need to cluster a matrix which contains zero values. I am clustering three separate sets of 24 values. The first two are non-zero and represent hourly ambient temperature (in K) and electrical ...
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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 ...
- 1,091
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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 ...
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285
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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 ...
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Does the cluster validity index based on global mean applicable to k-medoids clustering?
There are many cluster validity index (cvi) requiring the global mean in their calculations, such as the Calinski-Harabasz index. I was wondering is this type of cvis applicable to k-medoids ...
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K-medoids algorithm for time series with varying lengths
Can time series having multiple lengths be clustered using the k-medoids algorithm. I am essentially looking for a way to find a representative pattern from a set of time series using the k-medoids ...
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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
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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 ...
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Partitioning Around Medoids: Choosing a cluster number larger than the "optimal" one?
I asked a number of 71 'experts' to sort 92 different psychological constructs based on their similarity. Based on their answers, I constructed a dissimilarity matrix.
Initially, I wanted to analyse ...
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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/...
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Quality of PAM clustering
I have a mixed data and I have been searching for the best method to cluster it and I've chosen PAM. I am working btw with R. I've considered all the 17 variables of my data to cluster: 4 qualitative ...
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437
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Duplicated Rows in Mixed Data Type Clustering
I have a dataset which has ~200k rows and looks like the following -
...
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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 ...
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what`s the name or meaning of this clustering algorithm?
I have been learning the fuzzy clstering algorithm recently,and I got an object function as following:
\begin{array}{l}
\min \;\;J = \sum\limits_{i = 1}^N {\sum\limits_{k = 1}^K {\sum\limits_{j = 1}^...
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Is there a way to calculate the Explained Variance with PAM or k-medoids?
I am studying geographic solar radiation data obtained from satellite images. I would like to and use cluster analysis and make some correlation analysis by clusters, instead of by pixel - as some ...
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why does k-medoids ignore between-cluster distances
The k-medoids algorithm is a popular distance-based clustering algorithm. It uses a heuristic algorithm to assign samples to clusters based on centroids, which are itself samples. It's cost function ...
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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 ...
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Approximate medoid using matrix approximation
The medoid of a set of $n$ points is defined as the point that minimizes the average distance to all the other points. If there is a matrix containing rows in which each row is the set of all metric ...
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How to derive the time computational complexity of k-medoids (PAM) clustering algorithm?
I have read that the time complexity of k-medoids/Partitioning Around Medoids (PAM) is O(k(n-k)^2). I am trying to understand how this algorithms translates into this time complexity.
As per my ...
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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 ...
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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 ...
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Can Medoids be manually selected in PAM?
I'm currently running a cluster analyses on a dataset that contains variables of categorical and continuos values.I applied the well-established Gower’s dissimilarity coefficient to account for ...
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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 ...
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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 ...
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Does the distance metric used with K-Medoids needs to respect the triangle inequality?
I'd like to understand if the K-Medoids algorithm requires to be used with a distance metric that respects the triangle inequality. In particular I'd like to try to apply the K-Medoids algorithm with ...
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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 "...
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Weird Clusplot when plotting k-mediods clustering vector
The basic idea of the problem is that I need to cluster a set of points for which I have a dissimilarity matrix.
I have a dataset of around 4600 points (latitudes and longitudes). I have also ...
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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 ...
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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 ...
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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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543
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Clustering before or after ordination
Can someone explain the implications of performing clustering either before or after performing NMDS?
I have some ecological data and I am performing a clustering analysis to identify communities of ...
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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.
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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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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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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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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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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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FAMES in case of Dynamic Time Warping
I found this paper Using Pivots to Speed-Up k-Medoids Clustering in which authors explain how to use triangular geometry and cosine law to speed up search of new medoids in case of K-medoids.
My ...
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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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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 ...
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