Unsupervised Classification Using Immune Algorithm
This addresses classification problems in data analysis, but it appears incremental as it builds on existing clonal selection principles and compares to traditional methods like K-means.
The paper tackles unsupervised classification by proposing the Unsupervised Clonal Selection Classification (UCSC) algorithm, which is data-driven and self-adaptive, and shows it is more reliable and has higher classification precision compared to K-means on artificial and real-life datasets.
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.