UCI数据库使用说明

UCI机器学习数据库使用说明（转）2011-04-25 14:40UCI机器学习数据库的网址: /ml/数据库不断更新至2010年，是所有学习人工智能都需要用到的数据库，是看文章、写论文、测试算法的必备工具。数据库种类涉及生活、工程、科学各个领域，记录数也是从少到多，最多达几十万条。UCI数据可以使用matlab的dlmread或textread读取，不过，需要先将不是数字的类别用数字，比如1/2/3等替换，否则读入不了数值，当字符了。UCI数据库使用说明转自：/bbs/thread-37-1-1.html此目录包含数据集和相关领域知识（后面以简短的列表形式进行的注释），这些数据已经或能用于评价学习 算法 。每个数据文件 （*.data）包含以“属性-值”对形式描述的很多个体样本的记录。对应的*.info文件包含的大量的文档资料 。（有些文件_generate_ databases；他们不包含*.data文件。）作为数据集和领域知识的补充，在utilities目录里包含了一些在使用这一数据集时的有用资料。地址 /mlearn/MLRepository.html ，这里的UCI数据集可以看作是通过web的远程拷贝。作为选择，这些数据同样可以通过ftp获得， . 可是使用匿名登陆ftp。可以在pub/machine-learning-databases目录中找到。注意：UCI一直都在寻找可加入的新数据，这些数据将被写入incoming子目录中。希望您能贡献您的数据，并提供相应的文档。谢谢贡献过程可以参考DOC-REQUIREMENTS文件。目前，多数数据使用下面的格式 ：一个实例一行，没有空格，属性值之间使用逗号“,”隔开，并且缺少的值使用问号“?”表示。并请在做出您的贡献后提醒一下站点管理员： 下面以UCI中IRIS为例介绍一下数据集：ucidatairis中有三个文件：Isindex为文件夹目录，列出了本文件夹里的所有文件，如iris中index的内容如下：Index of iris18 Mar 1996 105 Index08 Mar 1993 4551 iris.data30 May 1989 2604 siris.data为iris数据文件，内容如下：5.1,3.5,1.4,0.2,Iris-setosa4.9,3.0,1.4,0.2,Iris-setosa4.7,3.2,1.3,0.2,Iris-setosa7.0,3.2,4.7,1.4,Iris-versicolor6.4,3.2,4.5,1.5,Iris-versicolor6.9,3.1,4.9,1.5,Iris-versicolor6.3,3.3,6.0,2.5,Iris-virginica5.8,2.7,5.1,1.9,Iris-virginica7.1,3.0,5.9,2.1,Iris-virginica如上，属性直接以逗号隔开，中间没有空格（5.1,3.5,1.4,0.2,），最后一列为本行属性对应的值，即决策属性Is介绍了irir数据的一些相关信息，如数据标题、数据来源、以前使用情况、最近信息、实例数目、实例的属性等，如下所示部分：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 Virginica9. Class Distribution: 33.3% for each of 3 classes.本数据的使用实例请参考其他论文，或本站后面的内容。对应的英文有：This is the UCI Repository Of Machine Learning Databases and Domain Theories= This is the UCI Repository Of Machine Learning Databases and Domain Theories 4 December 1995 : pub/machine-learning-databases /mlearn/MLRepository.html Librarian: Patrick M. Murphy ( ) 111 databases and domain theories (36MB) =This directory contains data sets and domain theories (the latter have beenannotated as such in the following brief listing) that have been or can beused to evaluate learning algorithms. Each data file (*.data) containsindividual records described in terms of attribute-value pairs. Thecorresponding *.info file contains voluminous documentation. (Some files_generate_ databases; they do not have *.data files.)In addition to data sets and domain theories, the utilities/ directorycontains utilities that you may find useful when using datasets in thisrepository.The contents of this repository can be viewed and remotely copied overthe web. The address is /mlearn/MLRepository.html. Alternatively, the contents of this repository can be remotely copied via ftp to . Enter anonymous for user id, and e-mail address (email=userhostuserhost/email) for password. These databases can be found by executing cd pub/machine-learning-databases.Notes:1. Were always looking for addition al databases, which can be written to the sub-directory named /incoming. Please send yours, with documentation. Thanks - See DOC-REQUIREMENTS for suggested documentation procedures. Presently, most databases have the following format: 1 instance per line, no spaces, commas separate attribute values, and missing values are denoted by ?. Also, please notify the site librarian ( ) after making a donation.2. Ivan Bratko requested that the databases he donated from the Ljubljana Oncology Institute (e.g., breast-cancer, lymphography, and primary-tumor) have restricted access. We are allowed to share them with academic institutions upon request. These databases (like several others) require providing proper citations be made in published articles that use them. Citation requirements are in each databases corresponding *.doc file. To access any of these databases, send email to . To aid you in deciding if you want any of these databases, the documentation files are available.3. An archive server may now be used to recieve via e-mail files in this repository. Installed on ics, it provides email access to files in our anonymous ftp/uucp area (ftp). If people have no other access to our archives, then they can send mail to: Commands to the server may be given in the body. Some commands are:helpsend find The help command replies with a useful help message.If you publish material based on databases obtained from this repository,then, in your acknowledgements, please note the assistance you received byusing this repository. Thanks - this will help others to obtain the samedata sets and replicate your experiments. We suggest the following pseudo-APAreference format for referring to this repository (LaTeXd): Murphy,P.M., & Aha,D.W. (1994). it UCI Repository of machine learning databases /mlearn/MLRepository.html . Irvine, CA: University of California, Department of Information and Computer Science.Patrick M. Murphy (Repository Librarian) -Brief Overview of Databases and Domain Theories:Quick Listing:1. annealing (David Sterling and Wray Buntine)2. Artificial Characters Database & DT (donated by Attilio Giordana)3-4. audiology (Ray Bareiss and Bruce Porter, used in Protos) 1. Original Version 2. Standardized-Attribute Version of the Original.5. auto-mpg (from CMU StatLib library)6. autos (Jeff Schlimmer)7. badges (Haym Hirsh)8. balance-scale (Tim Hume)9. balloons (Michael Pazzani)10. breast-cancer (Ljubljana Institute of Ontcology, restricted access)11. breast-cancer-wisconsin (Wisconsin Breast Cancer Dbase, Olvi Mangasarian) 1. Original version 2. Diagnostic data set 3. Prognostic data set12. bridges (Yoram Reich)13-21. chess 1. Partial generator of Quinlans chess-end-game data (kr-vs-kn) (Schlimmer) 2. Shapiros endgame database (kr-vs-kp) (Rob Holte) 3. king-rook-vs-king (Michael Bain, Arthur van Hoff) 4-9. Six domain theories (Nick Flann)22. Bach Chorales (time-series) database (Darrell Conklin)23. Connect-4 Database (John Tromp)24-25. Credit Screening Database 1. Japanese Credit Screening Data and domain theory (Chiharu Sano) 2. Credit Card Application Approval Database (Ross Quinlan)26. Ein-Dor and Feldmessers cpu-performance database (David Aha)27. Diabetes Data (Serdar Uckun, AI-M94)28. dgp-2 data generation program (Powell Benedict)29. Document Understanding (Donato Malerba)30. Nine small EBL domain theories and examples in sub-directory ebl31. Evlin Kinneys echocardiogram database (Steven Salzberg)32. flags (Richard Forsyth)33. function-finding (Cullen Schafers 352 case studies)34. glass (Vina Spiehler)35. hayes-roth (from Hayes-Roth2s paper)36-39. heart-disease (Robert Detrano)40. hepatitis (G. Gong)41. horse colic database (Mary McLeish & Matt Cecile)42. (Boston) Housing database (from CMU StatLib library)43. ICU data (Serdar Uckun, AIM-94)44. Image segmentation database (Carla Brodley)45. ionosphere information (Vince Sigillito) 46. iris (R.A. Fisher, 1936)47. isolet (Ron Cole and Mark Fantys database donated by Tom Dietterich)48. kinship (J. Ross Quinlan)49. labor-negotiations (Stan Matwin)50-51. led-display-creator (from the CART book)52. lenses (Cendrowskas database donated by Benoit Julien)53. letter-recognition database (created and donated by David Slate)54. liver-disorders (BUPA Medicals database donated by Richard Forsyth)55. logic-theorist (Paul ORorke)56. lung cancer (Stefan Aeberhard)57. lymphography (Ljubjana Institute of Oncology, restricted access)58-59. mechanical-analysis (Francesco Bergadano) 1. Original Mechanical Analysis Data Set 2. PUMPS DATA SET60 mobile robots (donated by Klingspor, Morik and Rieger)61-64. molecular-biology 1. promoter sequences (Towell, Shavlik, & Noordewier, domain theory also) 2. splice-junction sequences (Towell, Noordewier, & Shavlik, domain theory also) 3. protein secondary structure database (Qian and Sejnowski) 4. protein secondary structure domain theory (Jude Shavlik & Rich Maclin)65. MONKs Problems (donated by Sebastian Thrun)66. Moral Reasoner Database (donated by James Wogulis)67. mushroom (Jeff Schlimmer)68. MUSK databases (2) (donated by Tom Dietterich)69. othello domain theory (Tom Fawcett)70. Page Blocks Classification (Donato Malerba)71. Pima Indians diabetes diagnoses (Vince Sigillito) 72. Postoperative Patient data (Jerzy W. Grzymala-Busse)73. Primary Tumor (Ljubjana Institute of Oncology, restricted access)74. Qualitative Structure Activity Relationships (QSARs) (Ross King)75. Quadraped Animals (John H. Gennari)76. Servo data (Ross Quinlan)77. shuttle-landing-control (Bojan Cestnik)78. solar flare (Gary Bradshaw)79-80. soybean (from Ryszard Michalskis groups)81. space shuttle databases (David Draper)82. spectrometer (Infra-Red Astronomy Satellite Project Database, John Stutz)83. Sponge Database (Iosune Uriz and Marta Domingo)84. Statlog Project databases (7) (from Ross King,.)85 Student Loan relati