Cluster Density Performance
(AI Studio Core)
Synopsis
This operator is used for performance evaluation of the centroid based clustering methods. This operator delivers a list of performance criteria values based on cluster densities.Description
The centroid based clustering operators like the K-Means and K-Medoids produce a centroid cluster model and a clustered set. The centroid cluster model has information regarding the clustering performed. It tells which examples are parts of which cluster. It also has information regarding centroids of each cluster. The Cluster Density Performance operator takes this centroid cluster model and clustered set as input and evaluates the performance of the model based on the cluster densities. It is important to note that this operator also requires a SimilarityMeasure object as input. This operator is used for evaluation of non-hierarchical cluster models based on the average within cluster similarity/distance. It is computed by averaging all similarities / distances between each pair of examples of a cluster.
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 (Data table)This input port expects an ExampleSet. It is output of the Data to Similarity operator in the attached Example Process.
distance measure (Similarity Measure)This input port expects a SimilarityMeasure object. It is output of the Data to Similarity operator in the attached Example Process.
performance vector (Performance Vector)This optional input port expects a performance vector. A performance vector is a list of performance criteria values.
cluster model (Centroid Cluster Model)This input port expects a centroid cluster model. It is output of the K-Means operator in the attached Example Process. The centroid cluster model has information regarding the clustering performed. It tells which examples are part of which cluster. It also has information regarding centroids of each cluster.
Output
- example set (Data table)
The ExampleSet that was given as input is passed without any modifications to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
- performance vector (Performance Vector)
The performance of the cluster model is evaluated and the resultant performance vector is delivered through this port. A performance vector is a list of performance criteria values.
Tutorial Processes
Evaluating the performance of the K-Means clustering model
The 'Ripley-Set' data set is loaded using the Retrieve operator. Note that the label is loaded too, but it is only used for visualization and comparison and not for building the clusters. A breakpoint is inserted at this step so that you can have a look at the ExampleSet before the application of the K-Means operator. The 'Ripley-Set' has two real attributes; 'att1' and 'att2'. The K-Means operator is applied on this data set with default values for all parameters. A breakpoint is inserted at this step so that you can have a look at the results of the K-Means operator. You can see that two new attributes are created by the K-Means operator. The id attribute is created to distinguish examples clearly. The cluster attribute is created to show which cluster the examples belong to. As parameter k was set to 2, only two clusters are possible. That is why each example is assigned to either 'cluster_0' or 'cluster_1'.
The Data to Similarity operator is applied on the resultant ExampleSet. This generates a SimilarityMeasure object. The clustered ExampleSet, cluster model and the Similarity Measure object are provided as input to the Cluster Density Performance operator. The Cluster Density Performance operator evaluates the performance of this model and delivers a performance vector that has performance criteria values. The resultant performance vector can be seen in the results workspace.
