How to run AgglomerativeClustering on a big data in python? The Next CEO of Stack Overflow2019 Community Moderator ElectionClustering not producing even clustersHDBSCAN cluster: still unclear to me how to chose 'min_cluster_size`How to run PCA and KNN on big-dataHow does ML Clustering put to a practical real-world use?Bag of Visual WordsUsing local outlier factor score to detect outliers at run timeHow to solve online clustering problemMulti-label classification model in python?Kmeans cluster validation when I have labeled test dataClassify big changes in target variable
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How to run AgglomerativeClustering on a big data in python?
The Next CEO of Stack Overflow2019 Community Moderator ElectionClustering not producing even clustersHDBSCAN cluster: still unclear to me how to chose 'min_cluster_size`How to run PCA and KNN on big-dataHow does ML Clustering put to a practical real-world use?Bag of Visual WordsUsing local outlier factor score to detect outliers at run timeHow to solve online clustering problemMulti-label classification model in python?Kmeans cluster validation when I have labeled test dataClassify big changes in target variable
$begingroup$
I run AgglomerativeClustering on a sample of data and fit a model. then I decide to predict this fit for all of my data but I got MemoryError.
How can I run AgglomerativeClustering on a big dataset?
should I create a classification based on clusters label?!
python clustering
asked Mar 24 at 10:39
parvijparvij
485214
$endgroup$
add a comment |
$begingroup$
I run AgglomerativeClustering on a sample of data and fit a model. then I decide to predict this fit for all of my data but I got MemoryError.
How can I run AgglomerativeClustering on a big dataset?
should I create a classification based on clusters label?!
python clustering
asked Mar 24 at 10:39
parvijparvij
485214
$endgroup$
add a comment |
$begingroup$
I run AgglomerativeClustering on a sample of data and fit a model. then I decide to predict this fit for all of my data but I got MemoryError.
How can I run AgglomerativeClustering on a big dataset?
should I create a classification based on clusters label?!
python clustering
asked Mar 24 at 10:39
parvijparvij
485214
$endgroup$
I run AgglomerativeClustering on a sample of data and fit a model. then I decide to predict this fit for all of my data but I got MemoryError.
How can I run AgglomerativeClustering on a big dataset?
should I create a classification based on clusters label?!
python clustering
python clustering
asked Mar 24 at 10:39
parvijparvij
485214
asked Mar 24 at 10:39
parvijparvij
485214
asked Mar 24 at 10:39
parvijparvij
485214
asked Mar 24 at 10:39
parvijparvij
485214
asked Mar 24 at 10:39
parvijparvij
485214
485214
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You can't.
By definition, the algorithm needs O(n²) memory and O(n³) runtime.
This does not scale to big data.
Use a different algorithm. Or subsample your data.
Results don't necessarily get better just because you use more data. In many cases it really does not matter. The quality of estimating the mean grows with sqrt(n), so it quite quickly does not pay off to use more data, as this will only affect small digits of the result.
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
$endgroup$
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
You can't.
By definition, the algorithm needs O(n²) memory and O(n³) runtime.
This does not scale to big data.
Use a different algorithm. Or subsample your data.
Results don't necessarily get better just because you use more data. In many cases it really does not matter. The quality of estimating the mean grows with sqrt(n), so it quite quickly does not pay off to use more data, as this will only affect small digits of the result.
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
$endgroup$
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
add a comment |
$begingroup$
You can't.
By definition, the algorithm needs O(n²) memory and O(n³) runtime.
This does not scale to big data.
Use a different algorithm. Or subsample your data.
Results don't necessarily get better just because you use more data. In many cases it really does not matter. The quality of estimating the mean grows with sqrt(n), so it quite quickly does not pay off to use more data, as this will only affect small digits of the result.
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
$endgroup$
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
add a comment |
$begingroup$
You can't.
By definition, the algorithm needs O(n²) memory and O(n³) runtime.
This does not scale to big data.
Use a different algorithm. Or subsample your data.
Results don't necessarily get better just because you use more data. In many cases it really does not matter. The quality of estimating the mean grows with sqrt(n), so it quite quickly does not pay off to use more data, as this will only affect small digits of the result.
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
$endgroup$
You can't.
By definition, the algorithm needs O(n²) memory and O(n³) runtime.
This does not scale to big data.
Use a different algorithm. Or subsample your data.
Results don't necessarily get better just because you use more data. In many cases it really does not matter. The quality of estimating the mean grows with sqrt(n), so it quite quickly does not pay off to use more data, as this will only affect small digits of the result.
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
answered Mar 24 at 14:45
Anony-MousseAnony-Mousse
5,075625
5,075625
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
add a comment |
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
>Results don't necessarily get better just because you use more data. My_Answer: I ran the algorithm on 0.02% of my data and I got the result but the problem raised when I need to label all records. so, my problem is with predicting not fitting!!!
$endgroup$
– parvij
Mar 24 at 21:40
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
You can always train a classifier to predict the remaining points consistent with the clustered sample. For example, a nearest neighbor classifier. But if you think you need a label for every point, you are probably using clustering the wrong way. Don't trust the labels. Study the patterns found, but don't rely on them.
$endgroup$
– Anony-Mousse
Mar 24 at 22:08
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
I do clustering for marketing, I want the label for each point because I want to do some special activities for each cluster. so, I studied clusters, I found them useful but after that, I need to use labels! (thanks Anony)
$endgroup$
– parvij
Mar 26 at 19:14
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
$begingroup$
Define rules based on your findings to label the points exactly as you want them. Then apply these rules to your data.
$endgroup$
– Anony-Mousse
Mar 27 at 0:01
add a comment |
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