achieve

Measurements at the beginning of the 4th grade (when the national
 average is 4.0) and of the 6th grade in 25 schools in New Haven.

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

Bettina Gruen

achieve function

A data frame with 25 observations on the following 4 variables.<dl class="dl-horizontal">
 <dt><code>read4</code></dt><dd>4th grade reading.</dd>
 <dt><code>arith4</code></dt><dd>4th grade arithmetic.</dd>
 <dt><code>read6</code></dt><dd>6th grade reading.</dd>
 <dt><code>arith6</code></dt><dd>6th grade arithmetic.</dd>
 </dl>

Format

Achievement Test Scores for New Haven Schools — achieve

A data frame with 25 observations on the following 4 variables.<dl class='dl-horizontal'>
 <dt><code>read4</code></dt><dd>4th grade reading.</dd>
 <dt><code>arith4</code></dt><dd>4th grade arithmetic.</dd>
 <dt><code>read6</code></dt><dd>6th grade reading.</dd>
 <dt><code>arith6</code></dt><dd>6th grade arithmetic.</dd>
 </dl>

achieve: Achievement Test Scores for New Haven Schools

Description

Usage

Arguments

Format

References