论文标题
可浸出的组件聚类
Leachable Component Clustering
论文作者
论文摘要
聚类尝试将数据实例分配为几个独特的组，而属于共同分区的数据之间的相似性可以保留。此外，在许多Realworld应用中经常发生不完整的数据，并对模式分析产生不良影响。结果，开发了数据插补和处理的特定解决方案是为了进行数据的缺失值，并且知识开发的独立阶段被吸收以了解信息理解。在这项工作中，提出了一种新颖的方法，用于群集的不完整数据（称为可渗透成分聚类）。该提出的方法不是现有方法，而是处理贝叶斯对齐的数据归合，并在理论上收集丢失的模式。由于方程式的简单数字计算，在保持计算效率的同时，提出的方法可以学习优化的分区。在几个人工不完整的数据集上进行的实验表明，与其他最先进的算法相比，所提出的方法能够呈现出色的性能。
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.
