Problem Formulation

Consider the following scenario. A baseball manager wants to identify and group players on the team who are very similar with respect to several statistics of interest. Note that there is no response variable in this example. The manager simply wants to identify different groups of players. The manager also wants to learn what differentiates players in one group from players in a different group.

The data set for this example is located in SAMPSIO.DMABASE. The following table contains a description of the important variables.

Name	Model Role	Measurement Leve	Description
NAME	ID	Nominal	Player name
TEAM	Rejected	Nominal	Team at the end of 1986
POSITION	Rejected	Nominal	Positions played at the end of 1986
LEAGUE	Rejected	Binary	League at the end of 1986
DIVISION	Rejected	Binary	Division at the end of 1986
NO_ATBAT	Input	Interval	Times at bat in 1986
NO_HITS	Input	Interval	Hits in 1986
NO_HOME	Input	Interval	Home runs in 1986
NO_RUNS	Input	Interval	Runs in 1986
NO_RBI	Input	Interval	RBIs in 1986
NO_BB	Input	Interval	Walks in 1986
YR_MAJOR	Input	Interval	Years in the major leagues
CR_ATBAT	Input	Interval	Career times at bat
CR_HITS	Input	Interval	Career hits
CR_HOME	Input	Interval	Career home runs
CR_RUNS	Input	Interval	Career runs
CR_RBI	Input	Interval	Career RBIs
CR_BB	Input	Interval	Career walks
NO_OUTS	Input	Interval	Put outs in 1986
NO_ASSTS	Input	Interval	Assists in 1986
NO_ERROR	Input	Interval	Errors in 1986
SALARY	Rejected	Interval	1987 salary in thousands
LOGSALAR	Input	Interval	Log of 1987 salary

For this example, you set the model role for TEAM, POSITION, LEAGUE, DIVISION, and SALARY to Rejected. SALARY is rejected because this information is contained in the LOGSALAR variable. No target variables are used in a cluster analysis or self-organizing map (SOM). If you want to identify groups based on a target variable, consider a predictive modeling technique and specify a categorical target variable.