Tagged Questions

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.

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What is known about the efficiency of spectral clustering in case the data is not fully connected?

My question stems from the fact that the normalized cut can be very low in case we have a lot of connected components, because the cost of "cuts" is zero which eventually will lead to a a low N-cut ...
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1answer
5 views

Validity index for non convex clusters

I'm trying to measure clustering methods on unsupervised data. Among others, I'm using DBSCAN which can find non centered clusters. Is there any internal cluster validity index fitted for non-convex ...
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13 views

Using Canonical Correlation Analysis (instead of EFA/PCA) to reduce the dimensionality of two sets of variables prior to clustering/classification

I have two sets of paired continuous data obtained from two tests. My goal is to answer the following research questions: Q1. To what extent can results on one test be used to predict the results on ...
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1answer
14 views

R - how to transform the similarity matrix to distance matrix for performing hierarchical clustering?

I am trying to cluster nodes (C1, C2, C3...) of a graph using hclust and my similarity metric is number of links between nodes. I have data like ...
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16 views

Can we use kNN and k-mean at a same time?

I Get dataset of neighbours using kNN and then I want to apply k-mean on that dataset. By using this, is it possible that I get more accurate result? Is it logically correct that use kNN and then ...
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5 views

post-hoc testing in clustering analysis 1 feature

I've read that a post-hoc testing of the results of a clustering analysis is incorrect to perform. If I understood well this is true if the data are multidimensional and the purpose is to find out ...
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0answers
24 views

Deciding overall similarity measure at each step of hierarchical clustering in r

I want to decide the appropriate number of clusters after using hclust() and drawing a dendogram. I don't have any idea about how many clusters should be there ...
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1answer
70 views

Meaningful inference about data structure based on components with low variance in PCA

A lot of microbiome (microbial ecology) papers that I have come across use either principal component analysis (PCA) or principal coordinate analysis (PCoA) to make conclusions about the data. A lot ...
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19 views

Dealing with outliers: Clustering

I am working with a dataset in R that I will be doing cluster analysis on and I am trying to determine the best way to deal with the outliers.I have twelve variables and most variables have between ...
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0answers
12 views

Ward's clustering with Gower's metric [duplicate]

I calculated Gower distance matrix with daisy(cluster) function in R and than applied K-medoids with pam(cluster) function. I tried for cluster number k=3,4,5,6...20. But average silhouettes were ...
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0answers
31 views

Non-decaying eigenvalues in Kernel PCA with small kernel width

I noticed that when I use a small width kernel (RBF) with PCA, I get my desired result (clustering in this case), but I do not get a decay in the eigenvalues (they stay about the same value). Is that ...
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0answers
21 views

Normalization in case of clustering

Can normalization in this form be used (x−μ)/σ and should it be used in case of clustering? I have parameters on different scales and since I'm calculating the distances I need to perform feature ...
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24 views

Clustering based on correlations between survey questions

I'm trying to analyze a survey and find the questions that are most often answered in the same way. There are 29 questions, and I have a matrix with the correlations between each pair of variables. I ...
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1answer
23 views

Normalization problems - how to normalize in case of set of points while new points arriving

I'm having a procedure in which I perform clustering, and later, for each new example I test if that example belongs to some of existing clusters, by calculating distance to existing centroids. To ...
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0answers
17 views

Gaussian based clustering/partitioning, does it make sense with not much data?

I have a dataset about hourly aggregated mobile phone usage (#calls, #sms, #internetConnections) in one mobile cell. For example I have this data about activity at 8:00am: ...
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12 views

which distance for clustering

I've got 6000 reports that I've cleaned up. I've used 8 different steps to remove words of the reports. For each report, I've got a table of the following form: ...
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0answers
9 views

Advantages of using spectral clustering

One use of spectral clustering is that it is applicable to situations where distance is non metric. What are the other advantages of using spectral clustering?
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1answer
22 views

Can nonlinear clustering produce 'fake' results?

I know that overfitting in classification is possible when using, for instance, an RBF kernel, due to its infinite dimension. But, is it possible to get (in a similar manner) fake clustering results ...
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0answers
5 views

Using Quality metrics of BIRCH Clusters

What is significance of quality metrics of BIRCH Clusters Distance3 and Distance4. Appreciate if there are pointers are how to use Average Intra Cluster Distance (D3) and Average Inter Cluster ...
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0answers
7 views

Testing Cluster Assignment/Pattern Matching against BIRCH Clusters

I have a dataset of size >35K in size / >50 dimensions. Used BIRCH algorithm for clustering. While testing, the data points with which cluster formed is not matching i.e., The data point shows closer ...
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1answer
29 views

Why is k-medians typically used with Manhattan rather than Euclidean distance?

K-medians is typically used with Manhattan distance rather than Euclidean distance. Why is this?
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1answer
34 views

K means clustering of variable with multiple values

I have a sample data below that is from a large data set, where each participant is given multiple condition for scoring. ...
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1answer
10 views

Clustering Data majority is 0

I am performing a cluster analysis with a 4K by 200+ table and my data mostly looks like this: ...
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1answer
43 views

Gower distance with R functions; “gower.dist” and “daisy”

I have 9 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I know that the Gower distance is a good metric for datasets with mixed variables. I tried both daisy(cluster) and ...
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0answers
19 views

Normalize data for clustering

I am trying to perform clustering (planned to use K-means in R) on the data that contain both categorical and continuous variables. For example, my data contains 4 variables: gender (M and F), income ...
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1answer
49 views

PCA clustering results 'ruined' by standartization

I have some data that I want to classify. As an initial measure, I did PCA for the data and I saw two distinct clusters of my data. However, when standardizing the data, the two clusters disappear. ...
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0answers
27 views

K-means and maximum likelihood!

Is there any relation between k-means and the maximum-likelihood estimate in unsupervised learning? Any references would be appreciates! Thank you!
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0answers
9 views

Clustering cases on variables discovered in-sample via factor analysis?

My Data I have 2-hourly readings on approximately 10K sensors taken over the course of a year. The resulting time series look pretty similar day to day (though there are some longer term trends), and ...
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0answers
19 views

How the quality of clusters made in SPSS can be evaluated?

How the quality of clusters made in SPSS with the method "Two-step clustering" can be evaluated? Which test should be applied to be sure that the quality is good.
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18 views

Determining number of clusters using Hadoop and Mahout

I would like to use Mahouts clustering algorithm, such as Streaming K Means. But the thing is, this algorithm (and others such as Lloyd KMeans) require to specify the number of clusters. I have read ...
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2answers
40 views

Clustering a long list of strings (words) into similarity groups

I have the following problem at hand: I have a very long list of words, possibly names, surnames, etc. I need to cluster this word list, such that similar words, for example words with similar edit ...
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2answers
33 views

Validating clustering results with labeled data

I am working on a clustering algorithm and would like to validate its performance against a well-known and used dataset: the KDD-CUP 99 dataset ...
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22 views

Which clustering technique to use for mixed data

I want to do segmentation of my customers based on certain attributes. My data contains a mix of categorical (such as industry, products used, risk category, etc.) and continuous variables (such as ...
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0answers
22 views

K-medoids clustering with Manhattan distance

I have 17 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I read about Gower distance and applied it in R with "daisy" function. After having distance matrix I used K-medoids to ...
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0answers
35 views

Gower distance with R

I have 17 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I know that the Gower distance is a good metric for datasets with mixed variables. When I use daisy function in cluster ...
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1answer
34 views

Is it possible to combine several clustering results in a meaningful way?

The problem I face is somewhat awkward, I have 40,000 points in my dataset and I would like to cluster them hierarchically. But due to the limitation of my laptop(and R) in each run of clustering only ...
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2answers
60 views

Latent Class Analysis vs. Cluster Analysis - differences in inferences?

What are the differences in inferences that can be made from a latent class analysis (LCA) versus a cluster analysis? Is it correct that a LCA assumes an underlying latent variable that gives rise to ...
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0answers
34 views

Clustering methods for decision trees

My thesis work examines e-commerce data that is clustered using a decision tree, but I am uncertain about where to start. What algorithm or methods does one use to do this?
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3answers
114 views

Why are mixed data a problem for euclidean-based clustering algorithms?

Most classical clustering and dimensionality reduction algorithms (hierarchical clustering, principal component analysis, k-means, self-organizing maps...) are designed specifically for numeric data, ...
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1answer
13 views

Calculating distance comparing sets of frequencies

I have two sets of items, say A (with items a1, a2..) and B (with items b1,b2..). Each item in A appears with different frequency with items in B, so each item would have a list of B items with ...
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1answer
20 views

unsupervised clustering with “unclassified” items

I have data (some behavioral features, measured on some scales) on people. I want to cluster people based on these features. This is an unsupervised scenario, as I have no prior knowledge on the ...
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0answers
30 views

clustering analysis of large amount of time series

I would like to cluster a set of time series, which are composed of around 50000 different time series. Are there established algorithms/package that can handle this scalability problem?
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1answer
62 views

K-medoids clustering with Gower distance in R

I have both numeric and binary data in my data set with 73 observations. I read a lot about which distance metric and which clustering technique to use especially from this web site. I decided to use ...
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1answer
44 views

Can we use cluster analysis in multiple regression

I am quite new to Data Analytics. I was just wondering whether we can use cluster analysis in Multiple Regression. Let me give you a scenario so that it becomes easier to visualize. I have a dataset ...
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0answers
21 views

What metric can I use to indicate if a class should be split?

I have trained a classifier based on some training data. Now, when I add test data consider the possibility the datapoints do not strongly belong to a class. So much so, that it would make sense to ...
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0answers
5 views

Aggregating Results for pvclust [migrated]

What is the best way to summarize the means or medians of the clusters identified by pvclust? I have done it before with hclust using the cutree command to identify clusters. I have then aggregated ...
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23 views

(binary) Matrix completion with less known data

Recently, I meet such problems, I call it matrix completion problem. For example, the row denotes the users and the column denotes items. And If one user like the ...
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0answers
32 views

Can I say there would be only 2 groups with such features in this data and prove it using cluster analysis?

I assume that there would be two groups that will emerge within the data with my assumed features. I run cluster analysis and best number of cluster (elbow, dendrogram, etc.) shows it will be 2 and ...
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1answer
33 views

How to cluster data with repeated measurements?

Most clustering algorithms assume that data points in each row are independent. I have some data with repeated measurements from individuals. I can use a standard algorithm, and then check to see if ...
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0answers
16 views

Clustering multi-dimensional data with RapidMiner without knowing k

I am currently struggling to find an appropriate clustering for a data set with binominal label and 10 features (all of them continuous, but in different ranges). As far as handling the high ...