A Novel Tree Cluster and Classification Approach Based on Least Closed Tree

DOI

10.2991/nceece-15.2016.41How to use a DOI?

Keywords

Data mining; Tree mining; Closed tree pattern; Tree cluster; Tree classification

Abstract

The extensive application of tree model has made tree mining become a hot field in data mining research. As an important branch of tree mining, tree cluster and tree classification plays a fundamental analysis role in many areas. In this paper, a tree cluster and classification algorithm was proposed based on least closed tree, which effectively solved problems in large amount of data in practical application. The basic method is bringing forward least closed tree as the candidate cluster and classification feature, using dynamic threshold by similarity cluster to make tree cluster operation be more quick and accurate, meanwhile the concept of tree classification rule grade proposed is used in tree classification algorithm, so that the unknown tree structure could be predicted promptly. Experimental results show that the method has higher speed and efficiency than that of other similar ones especially when large number of tree nodes. Introduction

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xin Guo
PY  - 2015/12
DA  - 2015/12
TI  - A Novel Tree Cluster and Classification Approach Based on Least Closed Tree
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 202
EP  - 211
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.41
DO  - 10.2991/nceece-15.2016.41
ID  - Guo2015/12
ER  -