Weka简介

简介

Weka的全名是怀卡托智能分析环境（Waikato Environment for Knowledge Analysis），是一款免费的，非商业化（与之对应的是SPSS公司商业数据挖掘产品–Clementine ）的，基于JAVA环境下开源的机器学习（machine learning）以及数据挖掘（data minining）软件。它和它的源代码可在其官方网站下载。有趣的是，该软件的缩写WEKA也是New Zealand独有的一种鸟名，而Weka的主要开发者同时恰好来自New Zealand的the University of Waikato。

数据格式

Weka中自带的鸢尾花数据集：

% 1. Title: Iris Plants Database %  % 2. Sources: % (a) Creator: R.A. Fisher % (b) Donor: Michael Marshall (MARSHALL%) % (c) Date: July, 1988 %  % 3. Past Usage: % - Publications: too many to mention!!! Here are a few. % 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems" % Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions % to Mathematical Statistics" (John Wiley, NY, 1950). % 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. % (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. % 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System % Structure and Classification Rule for Recognition in Partially Exposed % Environments". IEEE Transactions on Pattern Analysis and Machine % Intelligence, Vol. PAMI-2, No. 1, 67-71. % -- Results: % -- very low misclassification rates (0% for the setosa class) % 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE  % Transactions on Information Theory, May 1972, 431-433. % -- Results: % -- very low misclassification rates again % 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II % conceptual clustering system finds 3 classes in the data. %  % 4. Relevant Information: % --- This is perhaps the best known database to be found in the pattern % recognition literature. Fisher's paper is a classic in the field % and is referenced frequently to this day. (See Duda & Hart, for % example.) The data set contains 3 classes of 50 instances each, % where each class refers to a type of iris plant. One class is % linearly separable from the other 2; the latter are NOT linearly % separable from each other. % --- Predicted attribute: class of iris plant. % --- This is an exceedingly simple domain. %  % 5. Number of Instances: 150 (50 in each of three classes) %  % 6. Number of Attributes: 4 numeric, predictive attributes and the class %  % 7. Attribute Information: % 1. sepal length in cm % 2. sepal width in cm % 3. petal length in cm % 4. petal width in cm % 5. class:  % -- Iris Setosa % -- Iris Versicolour % -- Iris Virginica %  % 8. Missing Attribute Values: None %  % Summary Statistics: % Min Max Mean SD Class Correlation % sepal length: 4.3 7.9 5.84 0.83 0.7826  % sepal width: 2.0 4.4 3.05 0.43 -0.4194 % petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) % petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) %  % 9. Class Distribution: 33.3% for each of 3 classes. @RELATION iris @ATTRIBUTE sepallength REAL @ATTRIBUTE sepalwidth REAL @ATTRIBUTE petallength REAL @ATTRIBUTE petalwidth REAL @ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica} @DATA 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 6.1,2.8,4.7,1.2,Iris-versicolor 5.4,3.0,4.5,1.5,Iris-versicolor 6.0,3.4,4.5,1.6,Iris-versicolor 5.1,2.5,3.0,1.1,Iris-versicolor 5.7,2.8,4.1,1.3,Iris-versicolor 6.3,2.5,5.0,1.9,Iris-virginica 6.5,3.0,5.2,2.0,Iris-virginica 6.2,3.4,5.4,2.3,Iris-virginica 5.9,3.0,5.1,1.8,Iris-virginica % % % 

关系声明

属性声明

分类属性

分类属性由 <nominal−specification> <script type=”math/tex” id=”MathJax-Element-35″> </script>列出一系列可能的类别名称并放在花括号中： $\left \{ <nominal-name1> , <nominal-name2> , <nominal-name3> , ...\right \} </nominal-name3> </nominal-name2> </nominal-name1>$。数据集中该属性的值只能是其中一种类别。
例如:如下的属性声明说明“outlook”属性有三种类别：“sunny”，“ overcast”和“rainy”。而数据集中每个实例对应的“outlook”值必是这三者之一。
@attribute outlook {sunny, overcast, rainy}
如果类别名称带有空格，仍需要将之放入引号中。

字符串属性

日期和时间属性

数据信息

数据信息中“@data”标记独占一行，剩下的是各个实例的数据。

稀疏数据

参考链接