WEKA Instructions
Overview
Download Weka - A simple and reliable Java-based software solution that can assist you in data mining or developing learning schemes, saving you time. Go to WEKA homepage and download the version that you want.; I downloaded the 64-bit windows that includes Oracle's 64-bit Java VM 1.8. My version is 3.8 which is the latest stable version (March, 2018).
WEKA is a data mining suite that is open source and is availablefree of charge. If you want to be able to changethe source code for the algorithms, WEKA is a good tool to use. It alsoreimplements many classic data mining algorithms, including C4.5 which iscalled J48 in WEKA. For more information, check out theWEKA web page. Downloading and Invoking WEKA
![Weka 3.8 download Weka 3.8 download](/uploads/1/2/6/3/126359555/110170210.jpg)
Mac Users sometimes have issues installing Weka. A helpful student emailedme with some helpful advice for Mac users. If you are getting errors when you try toinstall Weka, then go to 'System Preferences > Security & Privacy', and when thewindow pops up, go to the “Allow apps downloads from” area and then select“App store and identified developers”. This should provide the necessary access. For more information, you can consultthis article. Once you have installed Weka you may want to restore the original values.
WEKA Documentation and Tutorial
There is documentation for WEKA available from the WEKA web page.If you click on 'documentation' you will find many useful resources, includinga WEKA manual. If you scroll down, you will find direct links to the latest WEKA manual. As of 2-11-2018 that is version 3.8.2.The tutorial that I will use in class is avialable below (wewill only cover the Explorer mode):Note: The tutorial is now slightly out of synch with the version of Weka. For example, in the tutorial the term 'Neural network' is used but in WEKAit is now called 'Multilayer Perceptron'. Also, the results in the tutorialfor J48 on the iris data is without the discretization step (so if you followthe tutorial and discretize the variables, undo it before going on. Also, theresults are with the 'binary-split' flag set to true even though this apparentlyis not set in the tutorial. We will not be going over the 'User Classifier', sowhen you get to it you can skip to the Clustering section of the tutorial.
Inputting Data Into WEKA
You will need to input your dataset into WEKA. You start the process byclicking on 'Open Url' while in the 'Preprocess' tab of the Explorer (this istab that you are initially in when WEKA starts). You can then browse to thelocation of the data file on your PC (you can use 'Open URL' if the datasetis on the web). WEKA ideally would like an .arff file, which contains aheader that describes the variables and the data types of the variables,followed by the data itself. The format of the .arff file is availablefrom the various WEKA manuals.There are some sample data sets that come with WEKA that you can access and playwith. These are all in a data directory where the program is installed (onPCs, under 'program files' and on the LC machines they should be under/usr/local/share/weka-3.6.4/data). Thus, when you install them to your PC, youmight find them under C:/Program Files/Weka-3-6/data. But just in case youcannot find them, I have alocalcopy of the sample Weka databases. If you want to enter the url(using the 'Open URL' option), it is:http://storm.cis.fordham.edu/~gweiss/data-mining/weka-data/. Then simply add the filename at the end of this string (e.g., 'iris.arff').
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Very often you will want to use a dataset that does not have an .arff file.I think the best method may just be to manually create the arff file althoughyou can try to inmport a csv file.
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Creating a .arff file
You should familarize yourself with the format of a .arff file before youtry to create the .arff file. The format is fairly simple. You start with@relation, then have a bunch of @attribute statements, and then have a @datacommand, followed by the data, one record per line. Very often you will havea file with the data only. This could be a regular .csv file, or it could bea .data file associated with a C4.5 data set. Note that C4.5 uses .data filesfor the data and .names files to explain the format of the data. The .namesfile contains essentially the same info that goes into the top of the .arfffile, although in a different format.Some things to watch out for. As I found out, the .arff file should not haveany blank lines. A blank line after the @relation command (before the@attribute commands) causes an error! This may not be apparent from thedocumentation on the .arff file. I suggest that beyond just looking at the.arff file documentation, you look on the web for an actual .arff file.
You will need to edit the file with some type of text editor. Wordpad worksfine. Some text editors may not preserve the line breaks and will cause aproblem. If you are familar with linux, you could always edit it on linuxmachine with vi or emacs (but that is not necessary). On windows it may beeasiest to name the file with a .txt extention, since that makes it easierto open with a text editor like wordpad. But eventually you will need torename it to a .arff file extension. Renaming extensions in windows is notalways easy. Here is some info on how to do that:
You need to make the file extensions visible and editable, which it probablywill not be by default. The exact method depends on the version of Windows.In my current version I go to the Windows explorer window and then select the'tools' menu option, then 'Folder Options', then 'view', and then deselect thecheckbox for 'hide extensions for known file types.' When you do not hide theextensions, you can edit them easily and thus change them from .txt to .arffand back again.An alternative would be to copy it to a linux machine (maybe with Ftp) and then change the name and copy it back. But while this may seem like overkill, modifying the window file options so you can edit the extension can also betricky.
Now, in many cases you will want to create an .arff file from a .data and.names file, since many datasets follow the C4.5 format. You can copy the .datafile to a .txt extension. Thus, if you are working with adult.data, rename itto adult.txt (or figure out how to open adult.data with wordpad by using the'open with' option). Then copy the part of the .names file that names anddescribes the variables/features. Put that at the top of the .txt file. But now you have to essentially convert from the format for the .names fileto the format for the .arff header. The format are actually fairly similar. One difference is that the .names uses 'continuous' whereas the .arff uses'NUMERIC'. Also, the discrete features that specify the set of possible valuesdo not use curly brackets for the .names format but do for the .arff format. One should be able to convert about a dozen features from one format to anotherin a few minutes. However, there is one key difference. The .names file willlist the class variable first, even though it shows up in the last element inthe data record. The .arff file uses the more natural convention where theclass variable would show up in the corresponding position-- which in this casemeans it would be at the end. Thus, you need to move it from the first to the last position when converting from .names to .arff.
Be careful in the conversion process because the error messages are notvery helpful.
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If you want to use the .csv importer, then after you have hit the'Browse URL' button in WEKA Explorer andhave browsed to the correct directory, set the value in the 'Files of type'drop down list to choose '.csv data files.' Then click open. WEKA shouldsucessfully read in the data.
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Install Weka
- download from Weka(https://www.cs.waikato.ac.nz/ml/weka/downloading.html)
- double click to unzip the file you have download, i.g. weka-3-8-2-oracle-jvm.dmg.
- drag to [applications]
- But NOT TRUST by Mac, or the source is not from [App Store].
- *** All you need to do is go to [Security & Privacy] -> [Allow apps downloaded from:] -> Click [Open Anyway] to unblock it.
- And then you can OPEN Weka successfully! Bingo!