搜索资源列表
C45Driver
- c45决策树改进算法，主要用于数据挖掘中的聚类分析。对从事dm研究的人应该有用-C45 Decision Tree Algorithm, mainly for data mining of cluster analysis. Dm engaged in research should be useful
realDBSCAN
- 二维的DBSCAN聚类算法，输入（x,y）数组，搜索半径Eps，密度搜索参数Minpts。输出: Clusters，每一行代表一个簇，形式为簇的对象对应的原数据集的ID-two-dimensional clustering algorithm, the input (x, y) array, search radius Eps. Minpts density search parameters. Output : Clusters, each firm on behalf of a cluste
yckzx
- 一个用C语言编写的基于遗传K中心的数据聚类源代码。-a C language based on genetic data centers K cluster source.
Spider4dataanlysis
- ?Spider-matlab工具箱，为一良好的数据分析工具箱，内建核偏最小二乘回归(KPLS),径向基网络回归(RBFnet)等；支持向量机(SVC)分类；聚类分析等．-Spider-Matlab Toolbox for a good data analysis toolbox. Built-nuclear partial least squares (PLS) regression neural network (RBFnet); Support Vector Machine (SVC) c
cluster-3.6.5
- 一种数据聚类算法的源码，可以在模式识别和图像处理中试用。 -a data clustering algorithm source code, in pattern recognition and image processing trial.
K_MEANS
- This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers u
200412262259022
- k-meansy算法源代码。This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of clu
Data-Mining-PPT
- 这是一个数据挖掘PPT的详细介绍,包括分析预测，聚类分析，挖掘频繁模式、关联和相关等-PPT is a detailed descr iption of data mining, including the analysis and forecasting, cluster analysis, mining frequent patterns, association and correlation
k-means-iris
- 针对著名的UCI机器学习数据库中的iris data的kmeans聚类分析程序，具有代表性-For the well-known UCI machine learning repository of the iris data of kmeans cluster analysis procedure, a representative
k-centers
- 不同于k均值聚类的k中心聚类，2007年SCIENCE文章Clustering by Passing Messages Between Data Points 中的方法-Unlike k-means clustering of the k cluster centers, in 2007 SCIENCE article, Clustering by Passing Messages Between Data Points of the Method
K-meansNB
- ：将K—means算法引入到朴素贝叶斯分类研究中，提出一种基于K—means的朴素贝叶斯分类算法。首先用K— me．arks算法对原始数据集中的完整数据子集进行聚类，计算缺失数据子集中的每条记录与 个簇重心之间的相似度，把记 录赋给距离最近的一个簇，并用该簇相应的属性均值来填充记录的缺失值，然后用朴素贝叶斯分类算法对处理后的数据 集进行分类。实验结果表明，与朴素贝叶斯相比，基于K—means思想的朴素贝叶斯算法具有较高的分类准确率。-: K-means algorithm will
shujuwajue
- 数据挖掘中模糊聚类分析对医学新生计算机分层教育的应用研究.pdf-----vb的数据挖掘，别人的文章，大家共享-Fuzzy Cluster Analysis in Data Mining of Medical Education freshmen computer application layer. Pdf----- vb data mining, other people' s articles, share
General_neural_network_of_clustering_algorithm
- 模糊聚类虽然能够对数据聚类挖掘，但是由于网络入侵特征数据维数较多，不同入侵类别间的数据差别较小，不少入侵模式不能被准确分类。本案例采用结合模糊聚类和广义神经网络回归的聚类算法对入侵数据进行分类。 -Although fuzzy clustering to cluster the data mining, but the characteristics of the network intrusion data more dimensions, different invasion was l
FCM_synthetic_control.data
- 1. Synthetic Control Chart Time Series合成控制图时间序列 a) 数据集描述：该数据集包含600个实例，属性个数为6，已知分为6类。 b) 参数选择： 聚类数为6 加权指数m=2 最大迭代次数为1000，隶属度最小变化量1e-5 -1. Synthetic Control Chart Time Series Synthetic Control Chart Time sequence
cluster
- 聚类算法，对数据集进行聚类，得出训练后的胜出权值和所有权值-Clustering algorithm to cluster data sets, to win the right to come to training value and ownership of values
data-and-case
- 数据挖掘多个算法及数据，包括关联规则，基本分析、聚类分析、决策树、神经网络、统计方法等-Data mining algorithms and data, including association rules, fundamental analysis, cluster analysis, decision trees, neural networks, statistical methods
AP-Cluster
- AP聚类算法的C++代码实现，其中数据是文本读入iris数据，P值选取欧式矩阵最小值。显示结果为聚类后结果-AP clustering algorithm C++ code, in which data is read into the text iris data, P values selected European matrix minimum. Showing results clustering results after
cluster
- 聚类算法，包括特征提取 归一化，还有线性和非线性分类器，matlab下调试成功案例-clustering classification algorithm ，it include linear and nonlinear classifier and data preprocessing
Clustering
- 本代码实现多个方法对数据进行聚类，例如knn方法(This code implements multiple methods to cluster data, such as the KNN method)
cluster
- 对数据的一种聚类归类算法，内容理解较为简单(A clustering classification algorithm for data, content understanding is relatively simple)