  Clustering attempts to partition data instances into several distinctive
groups, while the similarities among data belonging to the common partition can
be principally reserved. Furthermore, incomplete data frequently occurs in many
realworld applications, and brings perverse influence on pattern analysis. As a
consequence, the specific solutions to data imputation and handling are
developed to conduct the missing values of data, and independent stage of
knowledge exploitation is absorbed for information understanding. In this work,
a novel approach to clustering of incomplete data, termed leachable component
clustering, is proposed. Rather than existing methods, the proposed method
handles data imputation with Bayes alignment, and collects the lost patterns in
theory. Due to the simple numeric computation of equations, the proposed method
can learn optimized partitions while the calculation efficiency is held.
Experiments on several artificial incomplete data sets demonstrate that, the
proposed method is able to present superior performance compared with other
state-of-the-art algorithms.
