breastcancer

<p>This data set contains case information from 699 patients from Wisconsin General Hospital who received examination for mammographic masses, which were classified as either benign or malignant.</p>

datasets

Implements recently developed projection
pursuit algorithms for finding optimal linear cluster
separators. The clustering algorithms use optimal
hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

David Hofmeyr

PPCI

Projection Pursuit for Cluster Identification

breastcancer function

<p>A list with entries $x (a 699x9 matrix with each row corresponding to an individual case) and $c (a vector of labels indicating whether the mass was diagnosed as benign (2) or malignant (4)).</p>

breastcancer: Discrimination of Cancerous and Non-Cancerous Breast Masses

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