Investigate the Correlation of Breast Cancer Dataset using Different Clustering Technique

TL;DR

This paper explores various clustering techniques and preprocessing methods to analyze breast cancer data in an unsupervised manner, aiming to enhance medical prognosis systems by identifying data correlations.

Contribution

It compares multiple clustering methods and preprocessing steps to determine the most effective approach for analyzing breast cancer datasets without prior training.

Findings

K-means and PAM clustering techniques reveal significant data correlations.

Preprocessing improves clustering accuracy and robustness.

The study provides insights for designing better prognosis systems.

Abstract

The objectives of this paper are to explore ways to analyze breast cancer dataset in the context of unsupervised learning without prior training model. The paper investigates different ways of clustering techniques as well as preprocessing. This in-depth analysis builds the footprint which can further use for designing a most robust and accurate medical prognosis system. This paper also give emphasis on correlations of data points with different standard benchmark techniques. Keywords: Breast cancer dataset, Clustering Technique Hopkins Statistic, K-means Clustering, k-medoids or partitioning around medoids (PAM)