Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 445-452.

• Big Data & Data Mining • Previous Articles     Next Articles

Symbolic Value Partition Algorithm Using Granular Computing

YANG Feng   

  1. School of Computer Science,Southwest Petroleum University,Chengdu 610500,China
  • Online:2019-02-26 Published:2019-02-26

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

CLC Number: 

  • TP311
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