Leachable Component Clustering

TL;DR

This paper introduces leachable component clustering, a novel method for clustering incomplete data that integrates data imputation with Bayes alignment, achieving efficient and superior performance over existing algorithms.

Contribution

The paper proposes a new clustering approach for incomplete data that combines data imputation and pattern collection using Bayes alignment, enhancing accuracy and efficiency.

Findings

Outperforms state-of-the-art algorithms on artificial incomplete datasets.

Handles data imputation and clustering simultaneously with simple computations.

Demonstrates superior clustering performance with theoretical pattern recovery.

Abstract

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.