搜索资源列表
rosetta
- 粗糙集工具箱 可以进行粗糙集上下近似集的计算，重要度等等 属性约简 决策分类-Toolbox rough sets rough sets can calculate the upper and lower approximations, importance, etc., etc. Classification Decision Attribute Reduction
matlabret
- 基于matlab的粗糙集工具箱，与网上可查的“matlab的粗糙集工具箱的设计”论文的软件-Matlab toolbox based on rough sets, and online to check the " matlab toolbox rough set design" paper software
shang
- 一种无监督的数据离散化方法，程序简单，运行时间短，效果比较显著-An unsupervised data discretization methods, procedures easy, run a short time, compared the effect of significantly
fs_entropy
- data reduction with fuzzy rough sets or fuzzy mutual information
fs_neighbor
- fuzzy rough sets or fuzzy mutual information
NRS_FW_FS
- 新加入条件属性对决策属性的依赖度计算函数。基于粗糙集。-The new conditions for accession to the decision attribute attribute dependency calculation functions. Based on rough sets.
7
- applications of rough sets
datareduct
- 基于香浓熵的属性简约，用于属性约简，以便分类-data reduction with fuzzy rough sets or fuzzy mutual information
22222222
- data reduction with fuzzy rough sets or fuzzy mutual information
rough-sets
- Rough set theory can be regarded as a new mathematical tool for imperfect data analysis.The theory has found applications in many domains,such as decision support, engineering, environment, banking, medicine and others.
matlab-data-mining
- 数据挖掘(Data Mining)阶段首先要确定挖掘的任务或目的。数据挖掘的目的就是得出隐藏在数据中的有价值的信息。数据挖掘是一门涉及面很广的交叉学科，包括器学习、数理统计、神经网络、数据库、模式识别、粗糙集、模糊数学等相关技术。它也常被称为“知识发现”。知识发现(KDD)被认为是从数据中发现有用知识的整个过程。数据挖掘被认为是KDD过程中的一个特定步骤，它用专门算法从数据中抽取模式(patter，如数据分类、聚类、关联规则发现或序列模式发现等。数据挖掘主要步骤是：数据准备、数据挖掘、结果的解释
BP
- 粗糙集与神经网络的结合，-Rough sets and neural networks combined,
Program
- 模糊逻辑变精度粗糙集算法程序，数据挖掘故障诊断规则，27条训练集，8属性，5类故障。-Rough set of variable precision fuzzy logic algorithm procedures, data mining fault diagnosis rules, 27 train sets, eight properties, five fault.
rough-sets--attributes-reduction
- 一个粗糙集的属性约简算法，有详细注解，并给出了简单实例，可以运行得出结果-rough sets attributes reduction
chengxu
- 自己编写的topsis做评估的小程序。文件另附粗糙集帮助进行对比。-topsis I have written a small program to do the assessment. Help file attached rough sets were compared.
rough-set-codes
- 这是天津大学胡清华老师在粗糙集邻域领域做的最经典的源码，同学们可以在此基础上学习和修改，入口程序已经写好，需要其他方法可以自己添加，MAIN.m是入口程序，参数的意思在子函数里讲的很明白，调用了featureselect_FW_fast.m用来属性约简，几个clsf_dpd文件是使用不同的距离公式来计算属性重要度，选择得到属性结果，使用crossvalidate.m十折交叉算法来计算计算算法精度，该段代码调用了几个分类器，C4_5.m是决策树，KNN.m是最近邻分类器，NEC.m是类似于KNN的
featureselect_FW_fast
- 粗糙集，属性简约，能够提供良好的测试，需要做一些参数及内容调整(Rough sets, attribute parsimony, can provide good testing and need to do some parameter and content tuning)