Top Down Clustering
Synopsis
This operator performs top down clustering by applying the inner flat clustering scheme recursively. Top down clustering is a strategy of hierarchical clustering. The result of this operator is an hierarchical cluster model.
Description
This operator is a nested operator i.e. it has a subprocess. The subprocess must have a flat clustering operator e.g. the K-Means operator. This operator builds a Hierarchical clustering model using the clustering operator provided in its subprocess. You need to have a basic understanding of subprocesses in order to apply this operator. Please study the documentation of the
The basic idea of Top down clustering is that all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. Top down clustering is a strategy of hierarchical clustering. Hierarchical clustering (also known as Connectivity based clustering) is a method of cluster analysis which seeks to build a hierarchy of clusters. Hierarchical clustering, is based on the core idea of objects being more related to nearby objects than to objects farther away. As such, these algorithms connect 'objects' (or examples, in case of an ExampleSet) to form clusters based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of the cluster. At different distances, different clusters will form. These algorithms do not provide a single partitioning of the data set, but instead provide an extensive hierarchy of clusters that merge with each other at certain distances.
嗨,策略erarchical clustering generally fall into two types:
- Agglomerative: This is a bottom-up approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. This type of clustering is implemented in RapidMiner as the
Agglomerative Clustering - Divisive: This is a top-down approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
Clustering is concerned with grouping together objects that are similar to each other and dissimilar to the objects belonging to other clusters. It is a technique for extracting information from unlabeled data and 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.
Input
example set
This input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process.
Output
cluster model
This port delivers the hierarchical cluster model. It has information regarding the clustering performed.
clustered set
The ExampleSet that was given as input is passed with minor changes to the output through this port. An attribute withidrole is added to the input ExampleSet to distinguish examples. An attribute withclusterrole may also be added depending on the state of theadd cluster labelparameter.
Parameters
Create cluster label
This parameter specifies if a cluster label should be created. If this parameter is set to true, a new attribute withclusterrole is generated in the resultant ExampleSet, otherwise this operator does not add theclusterattribute.
Max depth
This parameter specifies the maximal depth of the cluster tree.
Max leaf size
This parameter specifies the maximal number of items in each cluster leaf.