dentitio

Mammal's teeth divided into the 4 groups: incisors, canines, premolars
 and molars.

datasets

The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary distance
measures and centroid computation. Further cluster methods
include hard competitive learning, neural gas, and QT
clustering. There are numerous visualization methods for
cluster results (neighborhood graphs, convex cluster hulls,
barcharts of centroids, ...), and bootstrap methods for the
analysis of cluster stability.

Friedrich Leisch

flexclust

Flexible Cluster Algorithms

Evgenia Dimitriadou

dentitio function

A data frame with 66 observations on the following 8 variables.<dl class="dl-horizontal">
 <dt>top.inc:</dt><dd>top incisors</dd>
 <dt>bot.inc:</dt><dd>bottom incisors</dd>
 <dt>top.can:</dt><dd>top canines</dd>
 <dt>bot.can:</dt><dd>bottom canines</dd>
 <dt>top.pre:</dt><dd>top premolars</dd>
 <dt>bot.pre:</dt><dd>bottom premolars</dd>
 <dt>top.mol:</dt><dd>top molars</dd>
 <dt>bot.mol:</dt><dd>bottom molars</dd>
 </dl>

Format

Dentition of Mammals — dentitio

A data frame with 66 observations on the following 8 variables.<dl class='dl-horizontal'>
 <dt>top.inc:</dt><dd>top incisors</dd>
 <dt>bot.inc:</dt><dd>bottom incisors</dd>
 <dt>top.can:</dt><dd>top canines</dd>
 <dt>bot.can:</dt><dd>bottom canines</dd>
 <dt>top.pre:</dt><dd>top premolars</dd>
 <dt>bot.pre:</dt><dd>bottom premolars</dd>
 <dt>top.mol:</dt><dd>top molars</dd>
 <dt>bot.mol:</dt><dd>bottom molars</dd>
 </dl>

dentitio: Dentition of Mammals

Description

Usage

Arguments

Format

References