  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.
