利用粒计算的符号型数据分组算法

摘要/Abstract

摘要： 在数据挖掘领域,基于符号型数据分组的数据预处理是一个极富挑战性的问题,它给人们提供了一种更加简化的数据表现形式。在已往的研究中,相关学者提出了许多解决方案,例如,运用粗糙集的方法来解决这一问题。文中提出了一种基于粒计算的符号型数据分组算法,主要分为粒度生成和粒度选择两个阶段。在粒度生成阶段,对于每一条属性,以对应属性值的聚类为叶子节点,自底向上以二进制树的形式构建粒层,形成属性树森林。在粒度选择阶段,以信息增益为基础,对每棵树进行全局考虑,选取最优的粒层,选层结果就是符号型数据的分组结果。实验结果表明,本算法呈现出比已有算法更加平衡的层次结构和更加优秀的压缩效率,具有较好的应用价值。

关键词: 符号型, 粒计算, 数据分组, 信息增益

Abstract: In the field of data mining,data preprocessing based on symbolic data packets is a very challenging issue.It provides people with a more simplified representation of data.In the past research,researchers proposed many solutions,such as using rough set approach to solve this problem.In this paper,a symbolic data grouping algorithm based on grain computing was proposed,which is divided into two stages:granularity generation and granularity selection.At the stage of particle size generation,for each attribute,the tree is constructed from the bottom of the leaf with the cluster of corresponding attribute values as a binary tree,forming a forest of attribute trees.In the stage of granularity selection,each tree is globally considered on the basis of information gain,and the optimal grain layer is selected.The result of layer selection is the grouping result of symbolic data.Experimental results show that compared with the existing algorithms,this algorithm presents a more balanced hierarchy and more excellent compression efficiency,and has better application value.

Key words: Granular computing, Information gain, Symbol, Value partition

中图分类号:

TP311

杨烽. 利用粒计算的符号型数据分组算法[J]. 计算机科学, 2018, 45(11A): 445-452. https://doi.org/

YANG Feng. Symbolic Value Partition Algorithm Using Granular Computing[J]. Computer Science, 2018, 45(11A): 445-452. https://doi.org/

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