Unsupervised Classification Using Immune Algorithm

TL;DR

This paper introduces UCSC, an unsupervised classification algorithm inspired by immune system principles, which adapts its parameters to data for faster and more accurate classification than traditional methods like K-means.

Contribution

The paper presents UCSC, a novel self-adaptive unsupervised classification algorithm based on immune algorithms, demonstrating improved reliability and precision over K-means.

Findings

UCSC outperforms K-means in classification accuracy.

UCSC adapts parameters automatically to data.

UCSC is reliable across artificial and real datasets.

Abstract

Unsupervised classification algorithm based on clonal selection principle named Unsupervised Clonal Selection Classification (UCSC) is proposed in this paper. The new proposed algorithm is data driven and self-adaptive, it adjusts its parameters to the data to make the classification operation as fast as possible. The performance of UCSC is evaluated by comparing it with the well known K-means algorithm using several artificial and real-life data sets. The experiments show that the proposed UCSC algorithm is more reliable and has high classification precision comparing to traditional classification methods such as K-means.