basketball

This data set contains a series of statistics about basketball players:

datasets

The design of this package allows us to run different clustering packages and compare the results between them, to determine which algorithm behaves best from the data provided.

Luis Perez Martos

Clustering

Techniques for Evaluating Clustering

basketball function

A data frame with 96 observations on 5 variables:<dl class="dl-horizontal">
 This data set contains a series of statistics about basketball players:
 <dt>assists_per_minuteReal</dt><dd>average number of assistances per minute</dd>
 <dt>heightInteger</dt><dd>height of the player</dd>
 <dt>time_playedReal</dt><dd>time played by the player</dd>
 <dt>ageInteger</dt><dd>number of years of the player</dd>
 <dt>points_per_minuteReal</dt><dd>average number of points per minute</dd>
</dl>

Format

This data set contains a series of statistics (5 attributes) about 96
basketball players: — basketball

A data frame with 96 observations on 5 variables:<dl class='dl-horizontal'>
 This data set contains a series of statistics about basketball players:
 <dt>assists_per_minuteReal</dt><dd>average number of assistances per minute</dd>
 <dt>heightInteger</dt><dd>height of the player</dd>
 <dt>time_playedReal</dt><dd>time played by the player</dd>
 <dt>ageInteger</dt><dd>number of years of the player</dd>
 <dt>points_per_minuteReal</dt><dd>average number of points per minute</dd>
</dl>

basketball: This data set contains a series of statistics (5 attributes) about 96 basketball players:

Description

Usage

Arguments

Format