Partitioning data into subsets of objects according to their mutual "similarity," without using preexisting knowledge such as class labels. Clustered-standard-errors and/or cluster-samples should be tagged as such; do not use the "clustering" tag for them.
1
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1answer
16 views
Clustering Road Networks at various Radii
I have numerous road segments that belong to a city. Each road segment as a weight that is given to it due to its level of 'connectivity' (‘impact’from a small side street to a motorway). So, small ...
0
votes
3answers
33 views
Clustering mixed variables in SAS
I have following variables in my dataset:
Working hours (numerical:ordinal)
Effectiveness (categorical:ordinal ; 4 values-> (poor,average,good,best))
Satisfaction (categorical:ordinal ; 4 values-> ...
1
vote
2answers
48 views
Clustering with shape prior
EDIT STARTS
After seeing the comments and answers, I believe I started in a wrong direction. I have a set of rectangles, which I want to cluster as shown below. . The approach I took was to consider ...
0
votes
0answers
19 views
Sorting/Clustering similarity matrices
I wonder, what are the available libraries in R or Python to do correlation matrix clustering (sometimes it is referred to clustering).
I also, wonder, after clustering/grouping each point. What is ...
3
votes
0answers
30 views
Which variables are driving correlations within groups
I'm running an analysis on a few data sets that each typically have 100-200 cases measured across 120-160 variables - something similar to looking at gene expressions. Each variable is a non-centered ...
3
votes
1answer
34 views
hyperspherical nature of K means (and other squared error clustering method)
This famous paper points out that any squared error based clustering method (K means being the most common example) tends to generate hyperspherical clusters. However, it does not point to a ...
2
votes
1answer
108 views
+100
Select best set of binary variables for clustering known sample labels
I have a set of samples, for which I know the "true groups". For this samples I have about 200 binary variables, I would like to know a method to select the subset of variables, that gives me a ...
0
votes
2answers
30 views
clustering gene expression data
I have a question about clustering.
I' m managing gene expression microarray data and I would like to cluster them in classes.
I searched around to find the best clustering algorithm for my data, ...
0
votes
1answer
44 views
Fit of a normal distribution to a one-dimensional dataset in R
I've got a set of (continuous) values from a measurement, where each object should be either positive or negative, and I know that the values of the "negative" objects should be approximately normally ...
1
vote
2answers
57 views
Why is the k-means algorithm minimizing the within cluster variance?
I have read that the k-means algorithm tries to minimize the within cluster sum of squares (or variance). With some brainstorming, a question popped up. Why is it that k-means or any other clustering ...
1
vote
0answers
33 views
Cluster or factor analysis?
I read a lot of forum to understand this, but I'm only more confused. I have a database with some comorbidities and I want to see if they could be divided in groups. I did a cluster analysis and a ...
1
vote
1answer
45 views
Kmeans: Whether to standardise? Can you use categorical variables? Is Cluster 3.0 suitable?
I am running kmeans for a market research study, and I have a couple of questions:
Should I be standardizing my data, and if so, how? For example, one variable I have is product demand, which is ...
2
votes
0answers
36 views
Clustering longitudinal (trajectory) data
I am hoping to implement an unsupervised technique that identifies distinct clusters of individuals based on longitudinal data: 100 continuous or categorical variables measured at different ages.
A ...
1
vote
2answers
111 views
Why do we use k-means instead of other algorithms?
I researched about k-means and these are what I got: k-means is one of the simplest algorithm which uses unsupervised learning method to solve known clustering issues. It works really well with large ...
3
votes
1answer
51 views
Implementations of clustering with asymmetrical distance/similarity matrix
In my clustering problem I'm working with custom similarity measure and looking for any implementation of algorithms with asymmetrical distance or similarity matrix. I'm only interested in those that ...
0
votes
2answers
46 views
Time Series Similarity : Differing Lengths with R
I am experimenting with creating a distance matrix between time series for clustering and similarity searching. The main reference I am using is for the Similarity procedure in SAS (Paper). I would ...
1
vote
2answers
81 views
Cluster location data with kmeans++
I clustered data according to latitude and longitude.Use the kmeans++ for increasing the accuracy of kmeans. But still result does not change much.
Here is my db index graph for kmeans++
For kmeans
...
0
votes
1answer
26 views
Noise in clustering of high dimensional sparse data
Questions:
1) How to detect noise variables in high dimensional data?
2) Does the method that is presented below make sense?
3) What clustering methods are most insensitive to random variables in ...
1
vote
0answers
29 views
Using PCA to merge and grade correlated items
I have a real estates' condos sold dataset with the following fields
DOM: Date on the market
sellPct: Percentage difference between the original and final price.
other fields such as Exposure( ...
0
votes
1answer
30 views
Clustering method that is robust against inhomogeneous cluster sizes
I have data clustered in various sets whose amount of member variables and occupied area varies, the former among multiple magitudes.
Here is an example:
Set A is very concentrated and has 100000 ...
4
votes
2answers
63 views
How random are the results of the kmeans algorithm?
I have a question regarding the kmeans algorithm. I know kmeans is a randomized algorithm, but how random is it and what results can I expect. Suppose you have clustered a dataset into $4$ clusters, ...
-1
votes
0answers
11 views
How to transform correlation matrix into dissimilarity matrix for use in clustering? [duplicate]
How to transform correlation matrix into dissimilarity matrix for use in clustering ?
Corellation matrix contains Pearson correlation coefficients between pairs of time series, is it any standard way ...
-3
votes
0answers
38 views
How to calculate f-score, precision, recall and entropy for clustered data? [closed]
Any help will be greatly appreciated!
1
vote
2answers
51 views
Persistent cluster IDs over similar inputs with k-means
I have multiple kmeans plots that I have generated in R. Specifically I have $5$ weeks and I generate $1$ kmeans plot per week. ...
1
vote
1answer
40 views
How to explain how I divided a bimodal distribution based on kernel density estimation
I have a dataset of bimodal population. It contains a smaller peak, which is considered to be "bad", and a bigger peak. I try to separate the bad part of data from the rest of data. What I did was: ...
-1
votes
2answers
46 views
Books on cluster algorithms
I'm searching for books on the basic k-means and divisive clustering algorithms. I'm interested in the pros and cons of both. It's a part of my bachelors thesis, I have implemented both and need books ...
0
votes
2answers
55 views
Correlation Clustering
Correlation Clustering : Given a signed graph where the edge label indicates whether two nodes are similar (+) or different (−), the task is to cluster the vertices so that similar objects are ...
0
votes
0answers
18 views
What is the interpretation of the cophenetic correlation?
Do higher values indicate better quality clusterings? Also if you use three different linkage methods (i.e. ward, single, or complete) which one should you choose? The one with the highest cophenetic ...
0
votes
1answer
45 views
Clustering a dataset to get the most abnormal data [duplicate]
I have several datasets in R+, each containing two training and test sets. For example the following dataset. I want to train a classifier by using training data such that by applying the test data, I ...
0
votes
1answer
37 views
Using F1_score to measure cluster validity
I have clustered over 4000 textual files, and now I want to check and evaluate clusters. I want to use F-measure (a mix of recall and precision).
The formal definition of F1_score is:
$$
...
0
votes
0answers
42 views
how to used F-measure for cluster validity?
I have clustered over 4000 textual files, i want to check and evaluate clusters. Thus, i want to used F-measure (it mix of recall and precision). that mean when i calculated the precision and recall i ...
0
votes
1answer
43 views
Transforming the distance value from a center, to a probability value
Let $c_i$ be the center of a micro-cluster (i.e. we have many centers representing some fragments of clusters). Let $c_1$ be the center which is the closest to a new data-point $x$, such that ...
1
vote
0answers
37 views
Understanding the construction of Dirichlet process
I'm trying to understand the construction process of DP, however, with little background in measure theory, the original papers are hard to read, but I believe the ideas behind these papers can be ...
0
votes
0answers
12 views
Can I use 2 clusters as treatment binary variable?
If I used k-mean clustering method to make the data into 2 clusters according to some attributes of specific kind J, could I create a binary variable for the 2 clusters and explain it as a difference ...
0
votes
0answers
19 views
Clustering of Different Data Types and Dimensions
I'm looking for advice on the most appropriate (and hopefully fastest) Clustering method for a few different scenarios.
First of all, please do correct me if Clustering is not what I'm looking for.
...
0
votes
0answers
36 views
Plotting silhouette values for cluster analysis
Silhouette values are calculated for each observation/point/sample in the data. I could not understand how the graph is plotted using the $s(i)$ values for each $i^{th}$ observation. Can you give an ...
1
vote
2answers
100 views
Cluster analysis on ordinal data (Likert scale)
I want to do clustering of my data in R, using kmeans or hclust (I am a new R user).
My data is ordinal, Likert scale, to measure the causes of cost escalation. I have 41 causes "variables" that ...
0
votes
0answers
23 views
Creating a cluster map
Is it possible to create a cluster map by just using pairwise correlations? If we have $4$ variables than there are $\binom{4}{2}+4$ distinct pairwise correlations. So on both the x-axis and y-axis we ...
0
votes
0answers
52 views
Cluster Analysis - Assign Data to Cluster
I am new to cluster analysis and doing it for first time. I have created the 6 clusters by using Weka tools like this (for 4 attributes):
...
1
vote
1answer
47 views
Dummycoding based on clustering from OM distances
I'm using TraMineR to determine a certain clustering based on Optimal Matching distances:
...
2
votes
0answers
113 views
Using Davies-Bouldin index in clustering
I am clustering data using k-medoid. I used Davies–Bouldin index for $2$ to $n-1$ clusters. Here $n = 100$ (using smaller test case). I find minimal value of the index for 98 clusters. But the overall ...
4
votes
1answer
90 views
Finding a known number of circle centers that maximize the number of points within a fixed distance
I have a set of 2-D data where I want to find the centers of a specified number of centers of circles ($N$) that maximize the total number of points within a specified distance ($R$).
e.g. I have ...
0
votes
0answers
55 views
How to identify a new pattern in a URL with a machine learning algorithm (Text mining)
I am trying to identify new patterns after analyzing a number of URLs. So let's say, I am investigating the hypothetical website Yoohle.com and their URLs have the following structure.
domain = ...
0
votes
2answers
52 views
Non-distance metrics in hierarchical clustering? [closed]
What happens, intuitively, when one uses non-distance metrics to calculate the distance matrix that feeds into a standard hierarchical clustering algorithm?
What mistakes will the algorithm make and ...
1
vote
0answers
21 views
How to cluster temporal trajectories of various social network statistics
I have social network data for 100 open source repositories. The data consists of 12 snapshots (1 for every month of a year) of the networks for several repositories. Out of this I want to extract ...
4
votes
1answer
73 views
Confusion about scatter matrices
I am learning about evaluating clustering outcome and am confused about the scatter matrices. Hoping to get some help here.
The within-cluster scatter matrix $S_W$is defined as:
$$
S_W=\sum _{ k=1 ...
0
votes
0answers
55 views
How to find trends/correlations in a simple 2D dataset?
I have a dataset of approximately 100 objects. For each object, I have made measurements and have a handful of values (typically less than 10).
I also know several physical properties of my objects. ...
2
votes
1answer
58 views
Approach to cluster facebook users
I want to cluster Facebook user based on the number of mutual friends. If two users have more number of mutual friends then they are designated more closer to each other. I am thinking about using ...
-1
votes
1answer
79 views
way to determine best number of clusters weka
When using weka library for clustering ,Is there any way to find best number of clusters.
The EM methods are defined but how to use these methods in java code.
Please suggest
3
votes
2answers
153 views
Cluster Big Data in R and Is Sampling Relevant?
I'm new to data science and have a problem finding clusters in a data set with 200,000 rows and 50 columns in R.
Since the data have both numeric and nominal variables, methods like K-means which ...
