![]() The cluster model has information regarding the clustering performed. It is output of the K-Medoids operator in the attached Example Process. This input port expects a flat cluster model. in a marketing application we may be interested in finding clusters of customers with similar buying behavior. It is a technique for extracting information from unlabeled data and can be very useful in many different scenarios e.g. the Agglomerative Clustering operator.Ĭlustering is concerned with grouping together objects that are similar to each other and dissimilar to the objects belonging to other clusters. It cannot be applied on models created by the operators that produce a hierarchy of clusters e.g. This operator can only be applied on models produced by operators that produce flat cluster models e.g. ![]() Hierarchical clustering, on the other hand, creates a hierarchy of clusters. Flat clustering creates a flat set of clusters without any explicit structure that would relate clusters to each other. These distribution measures are explained in the parameters. Two distribution measures are supported: Sum of Squares and Gini Coefficient. how well the examples are distributed over the clusters. The Item Distribution Performance operator takes this cluster model as input and evaluates the performance of the model based on the distribution of examples i.e. It tells which examples are parts of which cluster. The clustering operators like the K-Means and K-Medoids produce a flat cluster model and a clustered set. It evaluates a cluster model based on the distribution of examples. SynopsisThis operator is used for performance evaluation of flat clustering methods. You are viewing the RapidMiner Studio documentation for version 9.4 - Check here for latest version Item Distribution Performance
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |