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一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

java weka.classifiers.trees.J48 -t $WEKAHOME/data/iris.arff

This prints out a decision tree classifier for the iris dataset 
and ten-fold cross-validation estimates of its performance. If you
don't pass any options to the classifier, WEKA will list all the 
available options. Try:

java weka.classifiers.trees.J48

The options are divided into "general" options that apply to most
classification schemes in WEKA, and scheme-specific options that only
apply to the current scheme---in this case J48. WEKA has a common
interface to all classification methods. Any class that implements a
classifier can be used in the same way as J48 is used above. WEKA
knows that a class implements a classifier if it extends the
Classifier class in weka.classifiers. Almost all classes in
weka.classifiers fall into this category. Try, for example:

java weka.classifiers.bayes.NaiveBayes -t $WEKAHOME/data/labor.arff

Here is a list of some of the classifiers currently implemented in
weka.classifiers:

a) Classifiers for categorical prediction:

weka.classifiers.lazy.IBk: k-nearest neighbour learner
weka.classifiers.trees.J48: C4.5 decision trees 
weka.classifiers.rules.PART: rule learner 
weka.classifiers.bayes.NaiveBayes: naive Bayes with/without kernels
weka.classifiers.rules.OneR: Holte's OneR
weka.classifiers.functions.SMO: support vector machines
weka.classifiers.functions.Logistic: logistic regression
weka.classifiers.meta.AdaBoostM1: AdaBoost
weka.classifiers.meta.LogitBoost: logit boost
weka.classifiers.trees.DecisionStump: decision stumps (for boosting)
etc.

b) Classifiers for numeric prediction:

weka.classifiers.functions.LinearRegression: linear regression
weka.classifiers.trees.M5P: model trees
weka.classifiers.rules.M5Rules: model rules
weka.classifiers.lazy.IBk: k-nearest neighbour learner
weka.classifiers.lazy.LWR: locally weighted regression

=================
Association rules
=================

Next to classification schemes, there is some other useful stuff in 
WEKA. Association rules, for example, can be extracted using the 
Apriori algorithm. Try

java weka.associations.Apriori -t $WEKAHOME/data/weather.nominal.arff

=======
Filters
=======

There are also a number of tools that allow you to manipulate a
dataset. These tools are called filters in WEKA and can be found
in weka.filters.

weka.filters.unsupervised.attribute.Discretize: discretizes numeric data
weka.filters.unsupervised.attribute.Remove: deletes/selects attributes
etc.

Try:

java weka.filters.supervised.attribute.Discretize -i
  $WEKAHOME/data/iris.arff -c last

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4. Database access:
-------------------

In terms of database connectivity, you should be able to use any
database with a Java JDBC driver. When using classes that access a
database (e.g. the Explorer), you will probably want to create a
properties file that specifies which JDBC drivers to use, and where to
find the database. This file should reside in your home directory or
the current directory and be called "DatabaseUtils.props". An example
is provided in weka/experiment (you need to expand wek.jar to be able
to look a this file). This file is used unless it is overidden by one
in your home directory or the current directory (in that order).

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5. The Experiment package:
--------------------------

There is support for running experiments that involve evaluating
classifiers on repeated randomizations of datasets, over multiple
datasets (you can do much more than this, besides). The classes for
this reside in the weka.experiment package. The basic architecture is
that a ResultProducer (which generates results on some randomization
of a dataset) sends results to a ResultListener (which is responsible
for stating whether it already has the result, and otherwise storing
results).

Example ResultListeners include:

weka.experiment.CSVResultListener: outputs results as
comma-separated-value files.
weka.experiment.InstancesResultListener: converts results into a set
of Instances.
weka.experiment.DatabaseResultListener: sends results to a database
via JDBC. 

Example ResultProducers include:

weka.experiment.RandomSplitResultProducer: train/test on a % split
weka.experiment.CrossValidationResultProducer: n-fold cross-validation
weka.experiment.AveragingResultProducer: averages results from another
ResultPoducer 
weka.experiment.DatabaseResultProducer: acts as a cache for results,
storing them in a database.

The RandomSplitResultProducer and CrossValidatioResultProducer make
use of a SplitEvaluator to obtain actual results for a particular
split, provided are ClassifierSplitEvaluator (for nominal
classification) and RegressionSplitEvaluator (for numeric
classification). Each of these uses a Classifier for actual results
generation. 

So, you might have a DatabaseResultListener, that is sent results from
an AveragingResultProducer, which produces averages over the n results
produced for each run of an n-fold CrossValidationResultProducer,
which in turn is doing nominal classification through a
ClassifierSplitEvaluator, which uses OneR for prediction. Whew. But
you can combine these things together to do pretty much whatever you
want. You might want to write a LearningRateResultProducer that splits
a dataset into increasing numbers of training instances.

To run a simple experiment from the command line, try:

java weka.experiment.Experiment -r -T datasets/UCI/iris.arff  \
  -D weka.experiment.InstancesResultListener \
  -P weka.experiment.RandomSplitResultProducer -- \
  -W weka.experiment.ClassifierSplitEvaluator -- \
  -W weka.classifiers.rules.OneR

(Try "java weka.experiment.Experiment -h" to find out what these
options mean)

If you have your results as a set of instances, you can perform paired
t-tests using weka.experiment.PairedTTester (use the -h option to find
out what options it needs).

However, all this is much easier if you use the Experimenter GUI.

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6. Tutorial:
------------

A tutorial on how to use WEKA is in $WEKAHOME/Tutorial.pdf. However,
not everything in WEKA is covered in the Tutorial, and the package
structure has changed quite a bit. For a complete list you have to
look at the online documentation $WEKAHOME/doc/packages.html.  In
particular, Tutorial.pdf is a draft from the "Data Mining" book (see
our web page), and so only describes features in the stable 3.0
release.

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7. Source code:
---------------

The source code for WEKA is in $WEKAHOME/weka-src.jar. To expand it, 
use the jar utility that's in every Java distribution.

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8. Credits:
-----------

Refer to the web page for an up-to-date list of contributors:

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

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9. Call for code and bug reports:
---------------------------------

If you have implemented a learning scheme, filter, application,
visualization tool, etc., using the WEKA classes, and you think it 
should be included in WEKA, send us the code, and we can put it
in the next WEKA distribution.

If you find any bugs, send a fix to [e-mail].
If that's too hard, just send a bug report to the wekalist mailing 
list.

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10. Copyright:
--------------

WEKA is distributed under the GNU public license. Please read
the file COPYING.

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