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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25 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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27 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
26 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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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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1answer
24 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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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
10 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
39 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
44 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
11 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
86 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
21 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
56 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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29 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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12 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
23 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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0answers
30 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
65 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
35 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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0answers
30 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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40 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
39 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
89 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
35 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
137 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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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
81 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
54 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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24 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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34 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
43 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
85 views

How is Weka calculating nominal attributes for K-means clustering?

I have both numeric and nominal variables in my dataset. Before applying clustering in Weka, I specified nominal variables to Weka and select K-means clustering. It is good that my nominal data seems ...
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1answer
82 views

How does the Gower distance calculate the difference between binary variables'?

I have 17 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I need to run a cluster analysis. I know that the Gower distance is a good metric for datasets with mixed variables. ...
0
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0answers
20 views

Selecting the right BIC value

I'm using the hddc for an assignment to find the optimal number of clusters. The dataset is 9-dimensional and consists of 200.000 rows, however, the BIC values that I'm getting are really high. How ...
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0answers
40 views

Inter-cluster variance

Can you please help me understand how is inter-cluster (between clusters) variance defined? As opposed to intra-cluster variance which is pretty straightforward, I have not managed to found a clear ...
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1answer
52 views

How to visualize cluster data in a scatter way

Having a clustered dataset, I want to visualize a scatter plot for two fields so every cluster is shown on the plane by its mean value (also good to have a radius equal to std). How does one do this ...
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2answers
94 views

k-means cluster, How to re-calculate centroid when using cosine similarity?

I have a requirement using k-means cluster method with cosine similarity instead of Euclidean distance. for example: ...
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0answers
25 views

Validating feature patterns of subgroups in clustering

I am trying to identify the typical developmental trajectories that participants follow in a learning task. The data set includes the performance of 1,000 learners over 10 trials each and, say, looks ...
3
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1answer
68 views

Weighted cases in a cluster analysis for cases in SPSS

I am conducting a cluster analysis (of cases) for a database which has weight attributed to the individual cases to ensure that it mirrors the general population in terms of sociodemographic ...
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1answer
23 views

What is the easiest way to evaluate k-means clustering?

I did clustering with k-means, but I haven't complete my project, now I have to evaluate the result of the k-means clustering, and I want to do that with the easiest way. does anyone have any ...
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1answer
57 views

Cluster interpretation

I'm running flat clustering algorithm on my dataset that contains numeric (not categorical) data. Is there a method that can give me interpretation of clusters, and emphasize what are the most ...
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1answer
27 views

How to map clustering results to known groups?

I have a data set, which form 3 known groups. I performed k-means clustering algorithm on the data set, setting the number of clusters to be 3 as well. I end up with 3 groups by k-means. Wishing to ...
-1
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1answer
52 views

Best clustering technique for outlier detection?

I have around 15-20 points every second, and I would like to detect outliers based on -their density along x-axis , that means if I am using k-mean clustering then I specify that in x-direction max ...
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0answers
35 views

KNN - does it use centroids?

I'm really confused now having read so many articles on KNN, I can't help but think I'm missing the obvious. Let's say I have persons P1 and P2, P3 are represented with attributes of height, weight ...
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2answers
93 views

Proper Statistical Test for Binary Data

I looking for the best statistical test to apply in a particular situation and I hope I can find here the answer(s) I'm looking for. First of all some details: I'm studying 33 different mutants of a ...
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1answer
20 views

Matrix reordering algorithms

I have a similarity matrix and I would like to apply an algorithm that reorders the entries based on their similarity. The aim is to move entries with high similarity closer to the main diagonal. The ...