Cluster Distance Performance
(AI Studio Core)
Synopsis
This operator is used for performance evaluation of centroid based clustering methods. This operator delivers a list of performance criteria values based on cluster centroids.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 Distance Performance operator takes this centroid cluster model and clustered set as input and evaluates the performance of the model based on the cluster centroids. Two performance measures are supported: Average within cluster distance and Davies-Bouldin index. These performance measures are explained in the parameters.
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 K-Medoids operator in the attached Example Process.
cluster model (Centroid Cluster Model)This input port expects a centroid cluster model. It is output of the K-Medoids 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.
performance (Performance Vector)This input port expects a Performance Vector.
Output
- performance (Performance Vector)
The performance of the cluster model is evaluated and the resultant Performance Vector is delivered through this port. The Performance Vector is a list of performance criteria values.
- example set (Data table)
The ExampleSet that was given as input is passed without changing 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.
- cluster model (Centroid Cluster Model)
The centroid cluster model that was given as input is passed without changing to the output through this port. This is usually used to reuse the same centroid cluster model in further operators or to view it in the Results Workspace.
Parameters
- main criterionThis parameter specifies the main criterion to use for performance evaluation.
- Avg. within centroid distance: The average within cluster distance is calculated by averaging the distance between the centroid and all examples of a cluster.
- Davies Bouldin: The algorithms that produce clusters with low intra-cluster distances (high intra-cluster similarity) and high inter-cluster distances (low inter-cluster similarity) will have a low Davies–Bouldin index, the clustering algorithm that produces a collection of clusters with the smallest Davies–Bouldin index is considered the best algorithm based on this criterion. Please notice that empty clusters will be ignored in the calculation of the Davies-Bouldin index.
- main criterion onlyThis parameter specifies if only the main criterion should be delivered by the performance vector. The main criterion is specified by the main criterion parameter
- normalizeThis parameter specifies if the results should be normalized. If set to true, the criterion is divide by the number of features.
- maximizeThis parameter specifies if the results should be maximized. If set to true, the result is not multiplied by minus one.
Tutorial Processes
Evaluating the performance of the K-Medoids 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 itself. A breakpoint is inserted at this step so that you can have a look at the ExampleSet before application of the K-Medoids operator. The 'Ripley-Set' has two real attributes; 'att1' and 'att2'. The K-Medoids 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-Medoids operator. You can see that two new attributes are created by the K-Medoids 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'. Also note the Plot View of this data. You can clearly see how the algorithm has created two separate groups in the Plot View. A cluster model is also delivered through the cluster model output port. It has information regarding the clustering performed. Under the Folder View you can see members of each cluster in folder format. You can see information regarding centroids under the Centroid Table and Centroid Plot View tabs.
The Cluster Distance Performance operator is applied to measure the performance of this clustering model. The cluster model and clustered set produced by the K-Medoids operator are provided as input to the Cluster Distance Performance operator which 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.
