Contents
Does clustering need a target variable?
The target variable is not used in the Variable Clustering node. It is an unsupervised method similar to Principal Component Analysis that only looks at the relationship among the input variables. Hope that helps!
Is clustering is used to predict continuous variable?
Many clustering algorithms (like DBSCAN or K-Means) use a distance measurement to calculate the similarity between observations. Because of this, certain clustering algorithms will perform better with continuous attributes. Height and weight are continuous attributes while Season is a categorical attribute.
How close are stars in a globular cluster?
about 1 light year
The typical distance between stars in a globular cluster is about 1 light year, but at its core the separation between stars averages about a third of a light year—13 times closer than Proxima Centauri, the closest star to the Sun. Globular clusters are thought to be unfavorable locations for planetary systems.
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 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.
Which is the best method for cluster analysis?
This suggests that Ward’s method may be preferred for the current data. In summary, this method is performed in essentially the same manner as the previous method the only difference is that the cluster analysis is based on Analysis of Variance instead of distances.