The Predictive Grouping category contains tools to group either records or fields into a smaller number of groups.
Append Cluster Tool: The Append Cluster tool appends the cluster assignments from a K-Centroids Cluster Analysis tool to a data stream.
Find Nearest Neighbors Tool: The Find Nearest Neighbors tool finds the selected number of nearest neighbors in the data stream that corresponds to each record in the query stream based on their Euclidean distance.
K-Centroids Cluster Analysis Tool: The K-Centroids Cluster Analysis tool represents a class of algorithms for partitioning cluster analysis. It works by taking the records in a database and dividing them into the best K groups based on some criteria.
K-Centroids Diagnostics Tool: The K-Centroids Diagnostic tool makes an assessment of the appropriate number of clusters to specify given the data and the selected clustering algorithm (K-Means, K-Medians, or Neural Gas).
MB Affinity Tool: The MB Affinity tool takes transaction data and constructs a matrix where each row is a transaction and the columns are the set of items that could appear in the transaction.
MB Inspect Tool: The MB Inspect tool, which is the second step of a Market Basket Analysis, takes the output of the MB Rules tool and provides a listing and analysis of those rules that can be filtered on several criteria in order to reduce the number or returned rules or item sets to a manageable number.
MB Rules Tool: The MB Rules tool, which is the first step of a Market Basket Analysis, takes transaction data and creates either a set of association rules or frequent item sets. A summary report of both the transaction data and the rules or item sets is produced, along with a model object that can be further investigated in a MB Inspect tool.
Multidimensional Scaling Tool: The Multidimensional Scaling tool separates univariate data based upon variance. The tool takes the dissimilarities, or distances, between items described in the data and generates a map between the items.
Principal Components Tool: The Principal Components tool allows the dimensions (the number of numeric fields) in a database to be reduced by transforming the original set of fields into a smaller set that accounts for most of the variance in the data.