Kmeans clustering with multiple columns containing strings Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsScikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)Combining K-means clustering with Agglomerative clusteringKMeans clustering to help label Multi-class Supervised modelConfused about how to apply KMeans on my a dataset with features extractedImplementation of kmeans clustering using RClustering for multiple variableClustering with multiple distance measureshow to convert multiple columns into single columns in pandas?Accuracy for Kmeans clusteringHow can I perform clustering on a list of words and ratings as columns?
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Kmeans clustering with multiple columns containing strings
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsScikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)Combining K-means clustering with Agglomerative clusteringKMeans clustering to help label Multi-class Supervised modelConfused about how to apply KMeans on my a dataset with features extractedImplementation of kmeans clustering using RClustering for multiple variableClustering with multiple distance measureshow to convert multiple columns into single columns in pandas?Accuracy for Kmeans clusteringHow can I perform clustering on a list of words and ratings as columns?
$begingroup$
I have the following dataset:
https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
What I want to find is clusters based on imdb score per genre per country. I have created a pandas data frame that contains per country for every unique genre the average imdb rating.
The dataframe looks like this:
country object
genre object
avgRating float64
dtype: object
Since the columns country and genre contain strings, I can't use Kmeans for this.
Is there anyway I can achieve what I want?
Ps: This is the first question I have asked. Tips on how I can improve my question are appreciated.
python k-means unsupervised-learning
asked Apr 2 at 21:58
DonCappieDonCappie
14
$endgroup$
add a comment |
$begingroup$
I have the following dataset:
https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
What I want to find is clusters based on imdb score per genre per country. I have created a pandas data frame that contains per country for every unique genre the average imdb rating.
The dataframe looks like this:
country object
genre object
avgRating float64
dtype: object
Since the columns country and genre contain strings, I can't use Kmeans for this.
Is there anyway I can achieve what I want?
Ps: This is the first question I have asked. Tips on how I can improve my question are appreciated.
python k-means unsupervised-learning
asked Apr 2 at 21:58
DonCappieDonCappie
14
$endgroup$
add a comment |
$begingroup$
I have the following dataset:
https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
What I want to find is clusters based on imdb score per genre per country. I have created a pandas data frame that contains per country for every unique genre the average imdb rating.
The dataframe looks like this:
country object
genre object
avgRating float64
dtype: object
Since the columns country and genre contain strings, I can't use Kmeans for this.
Is there anyway I can achieve what I want?
Ps: This is the first question I have asked. Tips on how I can improve my question are appreciated.
python k-means unsupervised-learning
asked Apr 2 at 21:58
DonCappieDonCappie
14
$endgroup$
I have the following dataset:
https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
What I want to find is clusters based on imdb score per genre per country. I have created a pandas data frame that contains per country for every unique genre the average imdb rating.
The dataframe looks like this:
country object
genre object
avgRating float64
dtype: object
Since the columns country and genre contain strings, I can't use Kmeans for this.
Is there anyway I can achieve what I want?
Ps: This is the first question I have asked. Tips on how I can improve my question are appreciated.
python k-means unsupervised-learning
python k-means unsupervised-learning
asked Apr 2 at 21:58
DonCappieDonCappie
14
asked Apr 2 at 21:58
DonCappieDonCappie
14
asked Apr 2 at 21:58
DonCappieDonCappie
14
asked Apr 2 at 21:58
DonCappieDonCappie
14
asked Apr 2 at 21:58
DonCappieDonCappie
14
14
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
After some more research we found this library: https://github.com/nicodv/kmodes.
The library k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the more well-known k-means algorithm, which clusters numerical data based on Euclidean distance.) The k-prototypes algorithm combines k-modes and k-means and is able to cluster mixed numerical / categorical data.
Because the dataframe contains categorical data we can't visualize it in a scatterplot. So I added the number representing the cluster the row was assigned to, for every row to get some form of visualization.
Normally you can only cluster ordinal data, because clustering happens based on distance. So I don't know to what extent this is reliable.
answered Apr 11 at 8:56
DonCappieDonCappie
14
$endgroup$
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
oldest
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active
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votes
$begingroup$
After some more research we found this library: https://github.com/nicodv/kmodes.
The library k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the more well-known k-means algorithm, which clusters numerical data based on Euclidean distance.) The k-prototypes algorithm combines k-modes and k-means and is able to cluster mixed numerical / categorical data.
Because the dataframe contains categorical data we can't visualize it in a scatterplot. So I added the number representing the cluster the row was assigned to, for every row to get some form of visualization.
Normally you can only cluster ordinal data, because clustering happens based on distance. So I don't know to what extent this is reliable.
answered Apr 11 at 8:56
DonCappieDonCappie
14
$endgroup$
add a comment |
$begingroup$
After some more research we found this library: https://github.com/nicodv/kmodes.
The library k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the more well-known k-means algorithm, which clusters numerical data based on Euclidean distance.) The k-prototypes algorithm combines k-modes and k-means and is able to cluster mixed numerical / categorical data.
Because the dataframe contains categorical data we can't visualize it in a scatterplot. So I added the number representing the cluster the row was assigned to, for every row to get some form of visualization.
Normally you can only cluster ordinal data, because clustering happens based on distance. So I don't know to what extent this is reliable.
answered Apr 11 at 8:56
DonCappieDonCappie
14
$endgroup$
add a comment |
$begingroup$
After some more research we found this library: https://github.com/nicodv/kmodes.
The library k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the more well-known k-means algorithm, which clusters numerical data based on Euclidean distance.) The k-prototypes algorithm combines k-modes and k-means and is able to cluster mixed numerical / categorical data.
Because the dataframe contains categorical data we can't visualize it in a scatterplot. So I added the number representing the cluster the row was assigned to, for every row to get some form of visualization.
Normally you can only cluster ordinal data, because clustering happens based on distance. So I don't know to what extent this is reliable.
answered Apr 11 at 8:56
DonCappieDonCappie
14
$endgroup$
After some more research we found this library: https://github.com/nicodv/kmodes.
The library k-modes is used for clustering categorical variables. It defines clusters based on the number of matching categories between data points. (This is in contrast to the more well-known k-means algorithm, which clusters numerical data based on Euclidean distance.) The k-prototypes algorithm combines k-modes and k-means and is able to cluster mixed numerical / categorical data.
Because the dataframe contains categorical data we can't visualize it in a scatterplot. So I added the number representing the cluster the row was assigned to, for every row to get some form of visualization.
Normally you can only cluster ordinal data, because clustering happens based on distance. So I don't know to what extent this is reliable.
answered Apr 11 at 8:56
DonCappieDonCappie
14
answered Apr 11 at 8:56
DonCappieDonCappie
14
answered Apr 11 at 8:56
DonCappieDonCappie
14
answered Apr 11 at 8:56
DonCappieDonCappie
14
14
add a comment |
add a comment |
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