Questions tagged [k-means]

k-means is a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.

Filter by
Sorted by
Tagged with
155
votes
13answers
200k views

K-Means clustering for mixed numeric and categorical data

My data set contains a number of numeric attributes and one categorical. Say, NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr, where ...
2
votes
2answers
7k views

Clustering users based on buying behaviour

I have a set of data which indicates purchase transaction of users (~1 million records). User can have more than 1 purchase across time. Data is spread over 6-7 months. Attributes that I have are ...
6
votes
3answers
27k views

How to test accuracy of an unsupervised clustering model output?

I am trying to test how well my unsupervised K-Means clustering properly clusters my data. I have an unsupervised K-Means clustering model output (as shown in the first photo below) and then I ...
59
votes
8answers
71k views

Clustering geo location coordinates (lat,long pairs)

What is the right approach and clustering algorithm for geolocation clustering? I'm using the following code to cluster geolocation coordinates: ...
10
votes
4answers
21k views

Clustering for mixed numeric and nominal discrete data

My data includes survey responses that are binary (numeric) and nominal / categorical. All responses are discrete and at individual level. Data is of shape (n=7219, p=105). Couple things: I am ...
15
votes
2answers
7k views

K-means vs. online K-means

K-means is a well known algorithm for clustering, but there is also an online variation of such algorithm (online K-means). What are the pros and cons of these approaches, and when should each be ...
6
votes
2answers
6k views

For which real world data sets does DBSCAN surpass K-means.?

For clustering, DBSCAN surpass k-means in terms of handling arbitrary shape data sets. In the most published papers about density based clustering, the experiments are performed with synthetic data ...
5
votes
1answer
4k views

Predictive analysis of rare events

I'm trying to predict rare events, meaning less than 1% of positive cases. I basically try to predict if a subject will have 0, 1, 2 ... , 6, > 6 failures (there are cases in all those categories). I'...
4
votes
3answers
2k views

Boundary conditions for clustering

I have some data that I would like to cluster with k-means. One of the features is the hour of the day. The problem is that the hour '23' is considered far from the hour '0'. How can I map the ...
4
votes
1answer
7k views

How to measure the similarity between two images?

I have two group images for cat and dog. And each group contain 2000 images for cat and dog respectively. My goal is try to cluster the images by using k-means. Assume image1 is ...
0
votes
3answers
2k views

How to cluster histograms or density distributions?

I have no idea where to start when it come to cluster distribution and finding out similar the similar one. Is there a package in R that does the job?
5
votes
1answer
70 views

How do I interpret my result of clustering?

I am working on a clustering problem. I have 11 features. My complete data frame has 70-80% zeros. The data had outliers that I capped at 0.5 and 0.95 percentile. However, I tried k-means (python) on ...
4
votes
1answer
654 views

What's the difference between finding the average Euclidean distance and using inertia_ in KMeans in sklearn?

I've found two different approaches online when using the Elbow Method to determine the optimal number of clusters for K-Means. One approach is to use the following code: ...
3
votes
1answer
89 views

What value can I gain by doing exploratory data analysis on features (and thus data) before doing clustering?

This might not be a very good question, but I would still ask if it's beneficial to do EDA before running a clustering algorithm? I understand that EDA helps us generate good and helpful insights ...
3
votes
1answer
997 views

K-means Clustering algorithm problems

I am trying to implement k-means clustering algorithm, but I am confused about calculating the distance and update(move) cluster centroids. For example, let's say that I have 2 features. One of them ...
2
votes
1answer
6k views

Accuracy for Kmeans clustering

I am looking for accuracy python code for kmeans clustering with no labels. Is there anyone who knows about it? it is ok that is not built-in function. Manually made is also ok
-1
votes
2answers
129 views

How to plot High Dimensional supervised K-means on a 2D plot chart

I'm Having a ML problem where my data set contains 80 features labelled into 3 groups (0, 1, -1). I want to plot the data on a 2D surface to see how "close" (similar) data with ...