Efficient Multi-Relational Clustering Algorithm

DENG Zuo-xiang; LI Chun-gui

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Efficient Multi-Relational Clustering Algorithm

更新时间：2026-09-04

- Efficient Multi-Relational Clustering Algorithm
- Vol. 33, Issue 4, Pages: 133-137(2016)
- 作者机构：
  
  1. 广西科技大学机构计算机科学与通信工程学院,广西,柳州,545006
  2. 上海交通大学电子信息与电气工程学院,上海,200240
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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邓左祥, 李春贵. 一种有效的多关系聚类算法[J]. 微电子学与计算机, 2016, 33(4): 133-137.

DENG Zuo-xiang, LI Chun-gui. Efficient Multi-Relational Clustering Algorithm[J]. 2016, 33(4): 133-137.
邓左祥, 李春贵. 一种有效的多关系聚类算法[J]. 微电子学与计算机, 2016, 33(4): 133-137. DOI：

DENG Zuo-xiang, LI Chun-gui. Efficient Multi-Relational Clustering Algorithm[J]. 2016, 33(4): 133-137. DOI：

摘要

研究多关系数据挖掘的聚类问题

提出一种有效的多关系聚类算法EMC.EMC算法的目标是提高聚类的准确率

并且降低运行时间.EMC算法首先利用元组ID传播的思想

计算两个对象之间的相似度

接着利用K中心点聚类算法

将对象划分成簇.实验表明

EMC算法显著降低运行时间

并且提高聚类的准确率.

Abstract

The problem of clustering in multi-relation data mining was investigated

and an efficient multi-relational clustering algorithm called EMC was proposed. EMC aims at increasing the accuracy of clustering

and decreasing running time. First

EMC computed the similarity between two objects by taking advantage of tuple ID propagation approach. Then

EMC clustered the objects by K-medoids clustering algorithm. Performance results demonstrate that

EMC significantly decreases running time

and increases the accuracy of clustering.

关键词

Keywords

references

Ling P, Rong X. Double-phase locality sensitive hashing of neighborhood development for multi-relational[C]// Proceedings of 2014 UK Workshop on Computational Intelligence. Hong Kong: UKCI, 2014: 206-213.

Dehaspe L, Raedt L. Mining association rules in multiple relations[C]// Proceedings of the 7th International Workshop on Inductive Logic Programming. Berlin: Springer, 1997: 125-132.

Houshmand M, Alishahi M. Improve the classification and Sales management of products using multi-relational data mining[C]// IEEE 3rd International Con-ference on Communication Software and Networks. Xi'an: IEEE, 2011: 329-337.

Yin X, Han J, Yu P. Cross-relational clustering with user's guidance[C]// Proceedings of the 11th ACM SIGKDD Conference on Knowledge Discovery in Data Mining. Chicago: ACM, 2005: 344-353.

New York Lavrac N, Dzeroski S. Inductive logic programming: techniques and applications[M]. New York: Ellis Horwood, 1994.

Han J, Kamber M. Data mining: concepts and techniques[M]. FAN M, MENG X, translated. 2nd ed. Beijing: China Machine Press, 2007: 373-382.

Vaghela V, Vandra K, Moni N. MR-MNBC: maxrel based feature selection for the multi-relational nave bayesian classifier[C]// Nirma University International Conference on Engineering. Ahmedabad: NUiCONE, 2013: 1-9.

Yin X, Han J, Yu P. LinkClus: efficient clustering via heterogeneous semantic links[C]// Proceedings of International Conference on Very Large Data Bases. Seoul: ACM, 2006: 427-438.

Nijssen S, Kok J. Faster association rules for multiple relations[C]// Proceedings of 17th International Joint Conference on Artificial Intelligence. Adelaide: ACM, 2001: 891-896.

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Related Institution

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