High Attribute Dimensional Sparse Clustering Recurrent Logical Neural Networks Model and Learning Algorithm

ZHOU Yong-quan; JIAO Li-cheng

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High Attribute Dimensional Sparse Clustering Recurrent Logical Neural Networks Model and Learning Algorithm

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- High Attribute Dimensional Sparse Clustering Recurrent Logical Neural Networks Model and Learning Algorithm
- Acta Electronica Sinica Vol. 32, Issue 8, Pages: 1342-1345(2004)
- 作者机构：
  
  1. 西安电子科技大学智能信息处理研究所,陕西,西安,710071
  2. 广西民族学院计算机与信息科学学院,广西,南宁,530006
  3. 西安电子科技大学智能信息处理研究所陕西西安,710071
  4. 广西民族学院计算机与信息科学学院广西南宁,530006
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP311.13
- Published：2004
- 稿件说明：
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ZHOU Yong-quan, JIAO Li-cheng. High Attribute Dimensional Sparse Clustering Recurrent Logical Neural Networks Model and Learning Algorithm[J]. Acta Electronica Sinica, 2004, 32(8): 1342-1345.
DOI：

ZHOU Yong-quan, JIAO Li-cheng. High Attribute Dimensional Sparse Clustering Recurrent Logical Neural Networks Model and Learning Algorithm[J]. Acta Electronica Sinica, 2004, 32(8): 1342-1345. DOI：

摘要

针对高属性维稀疏数据聚类问题

定义了模糊取大逻辑神经元

给出一种新的单层离散型回归逻辑神经网络模型

由稀疏特征差异度组成的相似阵作为该网络的初始权矩阵

通过单层离散型回归逻辑神经网络学习算法

可求出相似矩阵的等价阵

根据等价阵

给定不同的阈值

可动态地、有效地实现对高属性维稀疏数据的归并

使得聚类结果更符合实际情况

聚类质量较高.相比同类聚类算法

它具有学习、修正和应变功能

适用于大规模稀疏数据库和稀疏数据仓库的聚类分析.

Abstract

The concepts of high attribute dimensional information system and fuzzy logical neuron are firstly proposed

and a kind of new single layer discrete recurrent neural network model with

input and

output is designed.The learning of the weight matrix be done by means of finding weight matrix of equal matrix

and elements of initials weight matrix consist of sparse feature difference degree

so a sparse feature difference degree is given.It can realize dynamic state and effective classification

and the algorithm has important use in high attribute dimensional data mining and clustering analysis.

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