27
votes
K-Means clustering for mixed numeric and categorical data
(In addition to the excellent answer by Tim Goodman)
The choice of k-modes is definitely the way to go for stability of the clustering algorithm used.
The clustering algorithm is free to choose any ...
- 1,313
26
votes
Accepted
Confused about how to apply KMeans on my a dataset with features extracted
For clustering, your data must be indeed integers. Moreover, since k-means is using euclidean distance, having categorical column is not a good idea. Therefore you should also encode the column ...
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24
votes
K-Means clustering for mixed numeric and categorical data
This question seems really about representation, and not so much about clustering.
Categorical data is a problem for most algorithms in machine learning. Suppose, for example, you have some ...
- 575
18
votes
Accepted
How to measure the similarity between two images?
Check this handout!
Well, there a few so... lets go:
Given two images $J[x,y]$ and $I[x,y]$ with $(x,y) \in N^{N \times M}$...
A - Used in template matching:
Template Matching is linear and is not ...
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14
votes
Accepted
How to get the probability of belonging to clusters for k-means?
Let us briefly talk about a probabilistic generalisation of k-means: the Gaussian Mixture Model (GMM).
In k-means, you carry out the following procedure:
- specify k centroids, initialising their ...
- 1,105
13
votes
Accepted
Clustering high dimensional data
I don't think any of the clustering techniques "just" work at such scale. The most scalable supposedly is k-means (just do not use Spark/Mahout, they are really bad) and DBSCAN (there are some good ...
- 7,979
12
votes
Accepted
Clustering for multiple variable
K-means
Your data has $7$ dimensions so k-means is worth to try. See the PCA of your data and check if any cluster is visible there as K-means will have a tough time if clusters are not Gaussian. the ...
- 6,532
11
votes
Clustering for mixed numeric and nominal discrete data
Taking a stab:
I am trying to identify a clustering technique with a similarity measure that would work for categorical and numeric binary data.
Gower Distance is a useful distance metric when the ...
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10
votes
Clustering geo location coordinates (lat,long pairs)
GPS coordinates can be directly converted to a geohash. Geohash divides the Earth into "buckets" of different size based on the number of digits (short Geohash codes create big areas and longer codes ...
- 20.5k
10
votes
Accepted
K-Means vs hierarchical clustering
I would say hierarchical clustering is usually preferable, as it is both more flexible and has fewer hidden assumptions about the distribution of the underlying data.
With k-Means clustering, you ...
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10
votes
Accepted
How to evaluate the K-Modes Clusters?
The are some techniques to choose the number of clusters K.
The most common ones are The Elbow Method and The Silhouette Method.
Elbow Method
In this method, you ...
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9
votes
Clustering geo location coordinates (lat,long pairs)
I am probably very late with my answer, but if you are still dealing with geo clustering, you may find this study interesting. It deals with comparison of two fairly different approaches to ...
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9
votes
k-means in R, usage of nstart parameter?
nstart option attempts multiple initial configurations and reports on the best one. For example, adding nstart=25 will generate 25 initial random centroids and choose the best one for the algorithm. ...
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9
votes
Accuracy for Kmeans clustering
Accuracy is a measure of comparing the true label to the predicted label. K-Means is an unsupervised clustering algorithm where a predicted label does not exist. So, accuracy can not be directly ...
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8
votes
For which real world data sets does DBSCAN surpass K-means.?
I play around quite a bit with location data and have found examples both where k-means works fine and where k-means is a poor representation and DBSCAN is a great fit.
If you've ever gone hiking or ...
- 6,798
8
votes
Accepted
Sklearn: unsupervised knn vs k-means
Unsupervised k-NN
Unlike k-means, the unsupervised k-nn does not associate a label to instances. All it can do is tell you what instances in your training data is k-nearest to the point you are ...
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7
votes
Accepted
Boundary conditions for clustering
Since you accepted another answer, which says this can't be done, I am editing this to include an example of it being done. Hope this helps!
Original Answer:
The most logical way to transform hour ...
- 6,798
7
votes
K-Means clustering for mixed numeric and categorical data
You should not use k-means clustering on a dataset containing mixed datatypes. Rather, there are a number of clustering algorithms that can appropriately handle mixed datatypes. Some possibilities ...
7
votes
K-Means clustering for mixed numeric and categorical data
It depends on your categorical variable being used. For ordinal variables, say like bad,average and good, it makes sense just to use one variable and have values 0,1,2 and distances make sense here(...
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7
votes
K-means incoherent behaviour choosing K with Elbow method, BIC, variance explained and silhouette
Finding the elbow can be made more easier by computing the angles between the consecutive segments.
Replace your:
kIdx = 10-1
with:
...
- 71
7
votes
K-means incoherent behaviour choosing K with Elbow method, BIC, variance explained and silhouette
Just posting a summary of above comments and some more thoughts so that this question is removed from "unanswered questions".
Image_doctor's comment is right that these graphs are typical for k-means....
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7
votes
PCA before K-mean clustering
PCA reduces dimensionality. It does not change the number of observations you have. Nor does it change the order of the data. The n-th observation in your original dataset will still be the n-th ...
- 71
7
votes
Accepted
How to use k-means outputs (extracted features) as SVM inputs?
'Prediction' of k-means algorithm for each observation is just the corresponding centroid. So you can take vector of predicted centroids and use it as a categorical feature (maybe one-hot encoded).
...
- 1,541
7
votes
KMeans vs. DBSCAN
In short, KMeans is a distance based clustering technique where depending on the distance between the data points your initialization(usually kmeans++) and clustering works. In kmeans, you initialize ...
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6
votes
Clustering geo location coordinates (lat,long pairs)
You can use HDBSCAN for this. The python package has support for haversine distance which will properly compute distances between lat/lon points.
As the docs mention, you will need to convert your ...
- 181
6
votes
Accepted
Distributed k-means in Spark
In that link you posted, you can look at the python full solution here at the end and go through it to see what all is distributed.
In short, some parts are distributed, like reading data from the ...
- 1,041
6
votes
Accepted
Determinate K in K-Means Clustering
Wang, Kaijun, Baijie Wang, and Liuqing Peng. "CVAP: Validation for cluster analyses." Data Science Journal 0 (2009): 0904220071.:
To measure the quality of clustering results, there are two ...
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6
votes
Clustering users based on buying behaviour
Big Picture:
First of all, the feature set in your data is pretty sparse and uninteresting, so you should not expect to gain much traction from this problem. Use your human mind to think about the ...
- 6,798
6
votes
Accepted
How to test accuracy of an unsupervised clustering model output?
Since you have the actual labels, you can compare them with the obtained labels and evaluate performance. Typically purity and nmi (normalized mutual information) are used. Read this (Evaluation of ...
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