Matlab的数据挖掘工具箱spider

转自：http://blog.163.com/bit_runner/blog/static/53242218200961384725203/

Matlab的数据挖掘工具箱spider

一 spider主页http://www.kyb.mpg.de/bs/people/spider/ （也可以在google上搜索spider
matlab得到），关于它的介绍可以参考网址资料

二 使用时为matlab+spider+Weka；因为spider中的一些算法引用了Weka，比如j48

安装注意：

1 matlab7（R14）

6.5版本对java的支持不够，还没有开发javaclasspath等函数

??? Undefined function or variable 'javaclasspath'.

??? Undefined function or variable 'javaaddclasspath'.

2 jre1.4.2

matlab7自带的是1.4.2；matlab6自带的是1.3.可以在D:\MATLAB7\sys\java\jre\win32下看到。如果装了matlab7，使用它自带的1.4.2就可以了，尤其不要使用1.6，因为1.6太新了，matlab还不支持。可以在Matlab下使用 version -java查看JVM版本。

如果你想使用1.5的话，C:\Program Files\Java\jre1.5.0_10；把jre1.5.0_10这个文件夹拷贝到D:\MATLAB7\sys\java\jre\win32下，然后增加环境变量MATLAB_JAVA：D:\MATLAB7\sys\java\jre\win32\jre1.5.0_10。这一步如果有问题的话，重启Matlab会给出错误提示。找不到什么什么文件...

3 Weka3.4.10

使用weka版本低一些即可，高的不行，因为高版本的weka可能是用高版本的jvm支持的。

我使用的组合是 matlab7（R14）+jre1.4.2（matlab7自带的，不需要任何设置）+Weka3.4.10

weka 下载：http://www.cs.waikato.ac.nz/ml/weka/

三 使用方法

1 下载spider，有core和extra两个压缩包，把他们解压到同一个文件夹spider下面，然后放到$matlabroot\toolbox下面

2下载weka3.4.10，找到weka.jar放到$matlabroot\java\jar下面

3 启动Matlab打开$matlabroot\toolbox\spider\use_spider.m运行

提示spider的一些信息和 WEKA support enabled!表示成功了。

然后可以使用 help spider命令查看信息，他的功能列出如附录，然后就可以训练了。

四 一个简单的例子

X=rand(50)-0.5; Y=sign(sum(X,2));

dtrain=data(X,Y);

%生成训练集，也可以使用load()从文件读取

model=train(svm,dtrain));

%使用函数train（）训练模型

rtest=test(dtest,model);

%使用训练好的模型对验证集dtest测试，返回测试结果

五 附录spider信息

最新spider Version 1.71 (24/7/2006)

Basic library objects.

data - Storing input data and output results

data_global - Implementation of data object that limits memory overhead

algorithm - Generic algorithm object

group - Groups sets of objects together (algorithms or data)

loss - Evaluates loss functions

get_mean - Takes mean loss over groups of algs

chain - Builds chains of objects: output of one to input of another

param - To train and test different hyperparameters of an object

cv - Cross validation using objects given data

kernel - Evaluates and caches kernel functions

distance - Evaluates and caches distance functions

Statistical Tests objects.

wilcoxon - Wilcoxon test of statistical significance of results

corrt_test - Corrected resampled t-test - for dependent trials

Dataset objects.

spiral - Spiral dataset generator.

toy - Generator of dataset with only a few relevant features

toy2d - Simple 2d Gaussian problem generator

toyreg - Linear Regression with o outputs and n inputs

Pre-Processing objects

normalize - Simple normalization of data

map - General user specified mapping function of data

Density Estimation objects.

parzen - Parzen's windows kernel density estimator

indep - Density estimator which assumes feature independence

bayes - Classifer based on density estimation for each class

gauss - Normal distribution density estimator

Pattern Recognition objects.

svm - Support Vector Machine (svm)

c45 - C4.5 for binary or multi-class

knn - k-nearest neighbours

platt - Conditional Probability estimation for margin classifiers

mksvm - Multi-Kernel LP-SVM

anorm - Minimize the a-norm in alpha space using kernels

lgcz - Local and Global Consistent Learner

bagging - Bagging Classifier

adaboost - ADABoost method

hmm - Hidden Markov Model

loom - Leave One Out Machine

l1 - Minimize l1 norm of w for a linear separator

kde - Kernel Dependency Estimation: general input/output machine

dualperceptron - Kernel Perceptron

ord_reg_perceptron - Ordinal Regression Perceptron (Shen et al.)

splitting_perceptron - Splitting Perceptron (Shen et al.)

budget_perceptron - Sparse, online Pereceptron (Crammer et al.)

randomforest - Random Forest Decision Trees WEKA-Required

j48 - J48 Decision Trees for binary WEKA-Required

Multi-Class and Multi-label objects.

one_vs_rest - Voting method of one against the rest (also for multi-label)

one_vs_one - Voting method of one against one

mc_svm - Multi-class Support Vector Machine by J.Weston

c45 - C4.5 for binary or multi-class

knn - k-nearest neighbours

Feature Selection objects.

feat_sel - Generic object for feature selection + classifier

r2w2_sel - SVM Bound-based feature selection

rfe - Recursive Feature Elimination (also for the non-linear case)

l0 - Dual zero-norm minimization (Weston, Elisseeff)

fsv - Primal zero-norm based feature selection (Mangasarian)

fisher - Fisher criterion feature selection

mars - selection algorithm of Friedman (greedy selection)

clustub - Multi-class feature selection using spectral clustering

mutinf - Mutual Information for feature selection.

Regression objects.

svr - Support Vector Regression

gproc - Gaussian Process Regression

relvm_r - Relevance vector machine

multi_rr - (possibly multi-dimensional) ridge regression

mrs - Multivariate Regression via Stiefel Constraints

knn - k-nearest neighbours

multi_reg - meta method for independent multiple output regression

kmp - kernel matching pursuit

kpls - kernel partial least squares

lms - least mean squared regression [now obselete due to multi_rr]

rbfnet - Radial Basis Function Network (with moving centers)

reptree - Reduced Error Pruning Tree WEKA-Required

Model Selection objects.

gridsel - select parameters from a grid of values

r2w2_sel - Selecting SVM parameters by generalization bound

bayessel - Bayessian parameter selection

Unsupervised objects.

one_class_svm - One class SVM

kmeans - K means clustering

kvq - Kernel Vector Quantization

kpca - Kernel Principal Components Analysis

ppca - Probabilistic Principal Component Analysis

nmf - Non-negative Matrix factorization

spectral - Spectral clustering

mrank - Manifold ranking

ppca - Probabilistic PCA

Reduced Set and Pre-Image objects.

pmg_mds - Calculate Pre-Images based on multi-dimensional scaling

pmg_rr - Calculate Pre-Images based on learning and ridge regression

rsc_burges - Bottom Up Reduced Set; calculates reduced set based on gradient descent

rsc_fp - Bottom Up Reduced Set; calculates reduced set for rbf with fixed-point iteration schemes

rsc_mds - Top Down Reduced Set; calculates reduced set with multi-dimensional scaling

rsc_learn - Top Down Reduced Set; calculates reduced set with ridge regression

rss_l1 - Reduced Set Selection via L1 penalization

rss_l0 - Reduced Set Selection via L0 penalization

rss_mp - Reduced Set Selection via matching pursuit