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Community Bot
Community Bot
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One thing you could do is apply some dimensional reduction algorithm (such as PCA) so you can get the columns with high variance, then run k-means on that data set.

However, I suggest against using k-means in sparse matrices like yours. Anony-Mousse's answer herehere explains it well.

One thing you could do is apply some dimensional reduction algorithm (such as PCA) so you can get the columns with high variance, then run k-means on that data set.

However, I suggest against using k-means in sparse matrices like yours. Anony-Mousse's answer here explains it well.

One thing you could do is apply some dimensional reduction algorithm (such as PCA) so you can get the columns with high variance, then run k-means on that data set.

However, I suggest against using k-means in sparse matrices like yours. Anony-Mousse's answer here explains it well.

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masotann
masotann
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One thing you could do is apply some dimensional reduction algorithm (such as PCA) so you can get the columns with high variance, then run k-means on that data set.

However, I suggest against using k-means in sparse matrices like yours. Anony-Mousse's answer here explains it well.