You are viewing the RapidMiner Studio documentation for version 9.8 - Check here for latest version
K-Means (Kernel)
(RapidMiner Studio Core)
Synopsis
This operator performs clustering using the kernel k-means algorithm. Clustering is concerned with grouping objects together that are similar to each other and dissimilar to the objects belonging to other clusters. Kernel k-means uses kernels to estimate the distance between objects and clusters. K-means is an exclusive clustering algorithm.Description
This operator performs clustering using the kernel k-means algorithm. The k-means is an exclusive clustering algorithm i.e. each object is assigned to precisely one of a set of clusters. Objects in one cluster are similar to each other. The similarity between objects is based on a measure of the distance between them. Kernel k-means uses kernels to estimate the distance between objects and clusters. Because of the nature of kernels it is necessary to sum over all elements of a cluster to calculate one distance. So this algorithm is quadratic in number of examples and does not return a Centroid Cluster Model contrary to the K-Means operator. This operator creates a cluster attribute in the resultant ExampleSet if the add cluster attribute parameter is set to true.
Clustering is concerned with grouping together objects that are similar to each other and dissimilar to the objects belonging to other clusters. Clustering is a technique for extracting information from unlabeled data. Clustering can be very useful in many different scenarios e.g. in a marketing application we may be interested in finding clusters of customers with similar buying behavior.
