Leachable Component Clustering
This addresses the problem of incomplete data in clustering for pattern analysis applications, but it appears incremental as it builds on existing imputation and clustering methods.
The paper tackles clustering of incomplete data by proposing leachable component clustering, which uses Bayes alignment for imputation and recovers lost patterns, achieving superior performance compared to state-of-the-art algorithms on artificial datasets.
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.