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Can you cluster binary variables?
Yes, it is unlikely that binary data can be clustered satisfactorily. For binary data, the Euclidean distance measure used by K-Means reduces to counting the number of variables on which two cases disagree.
What is continuous and binary?
Identify quantitative data as discrete or continuous: Discrete data is countable (e.g. number of errors) and is often binary (having only two options) such as purchased/didn’t purchase. Continuous data provides more fidelity and therefore requires a smaller sample size to detect differences.
What is two step clustering?
Two-step cluster analysis identifies groupings by running pre-clustering first and then by running hierarchical methods. Because it uses a quick cluster algorithm upfront, it can handle large data sets that would take a long time to compute with hierarchical cluster methods.
Is binary qualitative?
For example, binary data, as introduced in many introductory texts or courses, certainly sound qualitative: yes or no, survived or died, present or absent, male or female, whatever. But score the two possibilities 1 or 0 and everything is then perfectly quantitative.
How to cluster binary data using continuous methods?
It describes both why applying continuous methods to binary data may inaccurately cluster the data, and more importantly what are some choices in appropriate distance functions. It does not answer how to cluster with k-means, but rather how to properly cluster binary data using non-Euclidean metrics and a hierarchical method like Ward.
Can You cluster based on mixed categorical data?
It could be that the continuous features available to you in your mixed data are adequate for grouping the data into representative clusters. So the first thing we’ll try here is to simply ignore our single categorical feature (which standard algorithms like k-means and DBSCAN don’t like), and only cluster based on our continuous features.
Can a hierarchical cluster be performed with binary variables?
Requesting more steps may result in stable assignments, but not necessarily meaningful ones. If all of the cluster variables are binary, then one can employ the distance measures for binary variables that are available for the Hierarchical Cluster procedure (CLUSTER command).
Is it possible to cluster binary data with k-means?
Yes, it is unlikely that binary data can be clustered satisfactorily. To see why, consider what happens as the K-Means algorithm processes cases. For binary data, the Euclidean distance measure used by K-Means reduces to counting the number of variables on which two cases disagree.