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Do you need to scale data for clustering?
In clustering, you calculate the similarity between two examples by combining all the feature data for those examples into a numeric value. Combining feature data requires that the data have the same scale.
Do we need to scale data for clustering?
In most cases yes. But the answer is mainly based on the similarity/dissimilarity function you used in k-means. If the similarity measurement will not be influenced by the scale of your attributes, it is not necessary to do the scaling job.
Do I need to scale data for clustering?
Do I need to scale data for K-means?
It depends on your data. If you have attributes with a well-defined meaning. Say, latitude and longitude, then you should not scale your data, because this will cause distortion. ( K-means might be a bad choice, too – you need something that can handle lat/lon naturally)
How to prepare data for clustering in machine learning?
While the Data Preparation and Feature Engineering for Machine Learning course covers general data preparation, this course looks at preparation specific to clustering. In clustering, you calculate the similarity between two examples by combining all the feature data for those examples into a numeric value.
What’s the best way to do clustering analysis?
Use your knowledge on the data, not random functionality available in your toolbox or on Wikipedia. Understand your data, then choose normalization based on data understanding. Also, I suggest to not run/consider clustering analysis until you have a good visualization of your data which looks like clusters.
When to use k-means or K-prototype clustering?
If you have categorical data, use K-modes clustering, if data is mixed, use K-prototype clustering. Data has no noises or outliers. K-means is very sensitive to outliers and noisy data. More detail hereand here.
Do you need to normalize data for clustering?
Based on the discussions, data normalization and removing correlation among data are often recommended. References posts: 1) Are mean normalization and feature scaling needed for k-means clustering?