Linked Questions

0
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1answer
1k views

Can I use Clustering with mixed data type in R? [duplicate]

I know there is same question in cross validated. But it is somewhat different. Clustering of mixed type data with R At there Q&A, as using daisy funtion(), we can use categorical data type in ...
2
votes
1answer
538 views

Clustering variables of mixed types in R [duplicate]

I need to analyse questionnaire survey data with mixed data types (nominal, ordinal, continuous). I want to cluster the variables. So far I only have dead ends. I know I can use daisy in the cluster ...
1
vote
0answers
274 views

Clustering on a data set with mixed variables [duplicate]

I have a data set consisting of $n$ elements with $d$ features for each element ($x_{i,f}$ means the value for the f-th feature of the i-th element). I would like to cluster this data set into $k$ ...
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0answers
12 views

How to deal with categorical variables in a clustering problem? [duplicate]

I have a dataset with the following variables (among ohter variables) that represents custome card transactions and I'm trying to cluster the clusters using k-means. ...
65
votes
7answers
68k views

Where to cut a dendrogram?

Hierarchical clustering can be represented by a dendrogram. Cutting a dendrogram at a certain level gives a set of clusters. Cutting at another level gives another set of clusters. How would you pick ...
36
votes
2answers
27k views

Hierarchical clustering with mixed type data - what distance/similarity to use?

In my dataset we have both continuous and naturally discrete variables. I want to know whether we can do hierarchical clustering using both type of variables. And if yes, what distance measure is ...
32
votes
3answers
18k views

What stop-criteria for agglomerative hierarchical clustering are used in practice?

I have found extensive literature proposing all sorts of criteria (e.g. Glenn et al. 1985(pdf) and Jung et al. 2002(pdf)). However, most of these are not that easy to implement (at least from my ...
11
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2answers
8k views

How do I know my k-means clustering algorithm is suffering from the curse of dimensionality?

I believe that the title of this question says it all.
4
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2answers
4k views

Convert categorical data to numerical data to compute a distance then

I want to calculate euclidean distances for a dataset I have. However, there are two attributes which are not numbers. One attribute is country(10 different countries), the other one is race(3 races). ...
1
vote
1answer
4k views

Why is clustering data with many categorical variables so slow?

I am trying to cluster a set of 160 points using 260,000 categorical variables (each variable has three possible values). I am trying to use the k-modes algorithm from the klaR package in R. It works ...
3
votes
3answers
1k views

Clustering numeric, categorical, and multivalue categorical data

I have data that look like this: ...
1
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3answers
1k views

How to measure cluster quality with distance matrix?

When performing clustering with an algorithm such as K-means, it's possible to construct a plot that shows the intra cluster variability according to the number of clusters to see if there is an elbow ...
4
votes
1answer
426 views

Is (a) multicollinearity and/or (b) binary variables an issue for DBSCAN? if so, how can one correct for these issues?

I have read some related questions, such as: Why are mixed data a problem for euclidean-based clustering algorithms?, What data structure to use for my cluster analysis or what cluster analysis to use ...
2
votes
0answers
316 views

Clustering for mixed data including string attributes [duplicate]

Suppose to have a dataset containing feature vectors representing some people. Each feature vector contains mixed type of attributes (e.g. sex, age, height, hair color, favourite film, ...). For ...
0
votes
1answer
140 views

Approach to clustering a large data frame (~7M * 60) with different data types

I posted a similar question yesterday. However, this question is distinct since I'm more seeking validation on my approach. I have a data frame with about 7M rows and 60 features. The features are a ...

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