Weka Download Mac
Mac OS X 10.8 (Mountain Lion) introduced a new security feature that, by default, limits "acceptable" applications to only those downloaded from the Mac App store. Thankfully, you can alter this in the system preferences. Go to "Security & Privacy" and change the "Allow applications downloaded from:" to "Anywhere". Weka will launch successfully after this change.
Weka Download Mac
Go to the releasespage on Github and download the Source code archive (zip or tar.gz) of therelease that you want to install. After the download finishes, decompress thearchive. Open a terminal/command prompt and execute the following commandfrom within the directory with the setup.py file:
Click here to download the Company Portal for Windows. This application can be used to download and install software like eduroam wireless, Microsoft Office, and Matlab onto your Windows laptop or desktop.
This software is no longer available for the download. This could be due to the program being discontinued, having a security issue or for other reasons.
Data professionals with an interest in furthering their data mining or analytics knowledge should make the effort to download, install, and explore the world of Weka. Its active support community is available to answer any questions or provide insight.
If you are working on Mac OS X 10.9.2 or newer, you may see a message about a software installer being damaged when you try to launch it. e.g. saying the software "is damaged and can't be opened. You should eject the disk image" or that the software "is damaged and can't be opened. You should move it to the Trash." Newer Mac systems include a security setting that can block the installation of apps downloaded from places other than the Mac App Store.To install QIAGEN software, you need to allow apps downloaded from identified developers as well the Mac App Store.Do this by adjusting your security settings:
We sign our software with a Developer ID from Apple. With the above setting chosen, you should be able to install our software.You will see a message warning you that the software has been downloaded from the internet, and asking if you wish to open it.This is expected, and you can proceed with installing the software.Security settings affect your whole system. If you generally do not want to allow apps downloaded from anywhere except the App Store, then change the security settings back to the desired setting after you have finished installing your QIAGEN software.If you continue to see this issue with the "Allow apps downloaded from" option set to "App Store and identified developers", please report this problem by emailing AdvancedGenomicsSupport@qiagen.com.Please include the full name of the installer, when you downloaded it and the URL of the page you visited to download it from.
In Mac OS X 10.9.2 and newer, there is a security setting that must be changed so that the downloaded installer can be opened. To change this setting on Mac 10.9.2 through 10.11.x, please take the following steps:
LightSIDE is anopen-source software suite for developing and testing text representations.It is available for Windows, Mac, and Linux operating systems. LightSIDEsupports several common methods of forming text features (e.g., unigram,bigram, trigram, phrases, stemming). It also includes an integrated versionof weka for testing text representations, however we won't be using that forthis homework assignment.
weka is a popularopen-source software suite for text and data mining that is availablefor Windows, Mac, and Linux operating systems. Weka supports avariety of categorization and clustering algorithms within a commonGUI (and programmable API), which makes it extremely convenient.
Epinions.com is a website wherepeople can post reviews of products and services. It covers a widevariety of topics. For this homework assignment, we downloaded aset of 12,000 posts about digital cameras and cars.
If this baseline is too big for weka to handle on your laptop, youcan reduce its size, for example, by pruning out any feature witha kappa value of 0.001 or less. If you prune your baseline featureset, be sure to discuss this in your report.
Test your baseline representations using J48, Naive Bayes, and SVM.Report Precision, Recall and F-measure (for the positivecategories) obtained with each baseline representation. Alsoreport the average time required to build models in weka for eachdataset for each baseline representation. Measurements of runningtime do not need to be super precise. The goal is for you to noticethe differences in running times for the different algorithms.
This seems like a lot of datasets. Do I really need to use them all? The goal is to let you see how text categorization behaves under different situations. There is a lot of variation in text categorization experiments. You can only see that by using several datasets, several learning algorithms, and several experiments. Fortunately, you only need to make a few clicks to convert text into features, load features into weka, and run an experiment in weka. Then, you read the values out of weka and enter them in a table. Easy!
These experiments should run on a machine with 2GB of RAM. If Weka refuses to launch with the new memory settings, shut down other programs that might be consuming memory. This should free up more memory that you can allocate to the Java virtual machine. It may also be helpful to shut down weka between experiments, so that it is forced to free memory from old experiments.
I have lots of memory on my computer, can I make weka go faster?The default settings for the Weka instances that run through DNDW is set to be at a 2GB max heap size. You may edit the configuration.bat file inside DNDW to increase the SET maxheap=2048M value to something larger than 2GB (note the new number has to be a power of 2 eg. 4096M).
The above examples help us to understand a little bit what an algorithm is and how it works. These algorithms are available at the weka tool. So we want to do only one thing: create a dataset and load it to that tool.
Attribute selection means that if we find the rice type, we measure some parameters rice size, rice colour, rice length, rice width. These measurements help us to find that type. The same thing weka tool also supports attribute selection via information gain using the InfoGainAttributeEval Attribute Evaluator. Like the correlation technique above, the Ranker Search Method must be used.
For this assignment you will need to use Weka - Data Mining Software in Java.You may download and install your own version of Weka (for Linux, Windows or Mac OS X) from this site: You may also use Weka software (for Linux) which I installed in my directory at/home/faculty5/ipivkina/weka-3-4/ The site provides a lot of information and documentation on Weka. Please use it.In order to run, Weka needs Java to be installed. I installed a more recent version of Java at/home/faculty5/ipivkina/jdk1.5.0_01/bin/java Feel free to use it.To run Weka you may typejava -jar weka.jar(add path to java and weka.jar in the above command if needed).Weka software contains an implementation of the Apriori algorithm for learning association rules. Association rules are of the form LHS ==> RHS where LHS and RHS are sets of attribute-value pairs. These are called item sets: an attribute-value pair is called an item.For example: rule 1 : outlook=sunny==> play=norule 2 : temperature=cool windy=FALSE 2 ==> humidity=normal play=yesEssentially, Apriori attempts to associate itemsets on the LHS with item sets on the RHS. Weka's Apriori association rule algorithmApriori works with categorical values only. Therefore, if a dataset containsnumeric attributes, they need to be converted into nominal before applying the Apriori algorithm. For this part of the assignment we will use aversion of the weather dataset weather.nominal.arff. The datasets are in weka-3-4/data directory (in directory /home/faculty5/ipivkina/weka-3-4/data/ if you are using my version).Make sure you work with copies of the datasets if you are requested tomodify them. Apply the Apriori algorithm to the nominal weatherdataset using Weka's command line interface (CLI).java weka.associations.Apriori -t data/weather.nominal.arffYou should see output like the following:Apriori=======Minimum support: 0.15Minimum metric : 0.9Number of cycles performed: 17Generated sets of large itemsets:Size of set of large itemsets L(1): 12Size of set of large itemsets L(2): 47Size of set of large itemsets L(3): 39Size of set of large itemsets L(4): 6Best rules found: 1. outlook=overcast 4 ==> play=yes 4 conf:(1) 2. temperature=cool 4 ==> humidity=normal 4 conf:(1) 3. humidity=normal windy=FALSE 4 ==> play=yes 4 conf:(1) 4. outlook=sunny play=no 3 ==> humidity=high 3 conf:(1) 5. outlook=sunny humidity=high 3 ==> play=no 3 conf:(1) 6. outlook=rainy play=yes 3 ==> windy=FALSE 3 conf:(1) 7. outlook=rainy windy=FALSE 3 ==> play=yes 3 conf:(1) 8. temperature=cool play=yes 3 ==> humidity=normal 3 conf:(1) 9. outlook=sunny temperature=hot 2 ==> humidity=high 2 conf:(1)10. temperature=hot play=no 2 ==> outlook=sunny 2 conf:(1)Description of OutputThe default values for Number of rules, the decrease forMinimum support (delta factor) and minimum Confidencevalues are 10, 0.05 and 0.9. Rule Support is theproportion of examples covered by the LHS and RHS whileConfidence is the proportion of examples covered by the LHSthat are also covered by the RHS. So if a rule's RHS and LHS covers50% of the cases then the rule has 0.5 support, if the LHS of a rulecovers 200 cases and of these the RHS covers 50 cases then theconfidence is 0.25. With default settings Apriori tries to generate 10rules by starting with a minimum support of 100%, iterativelydecreasing support by the delta factor until minimum non-zero supportis reached or therequired number of rules with at least minimum confidence has beengenerated. If we examineWeka's output, a Minimum support of0.15 indicates the minimum support reached in order to generate the 10rules with the specified minimum metric, here confidence of 0.9. The item set sizes generated are displayed; e.g. there are 6 four-item setshaving the required minimum support. Bydefault rules are sorted by confidence and any ties are broken basedon support. The number preceding indicates the number of casescovered by the LHS and the value following the rule is the number ofcases covered by the RHS. The value in parenthesis is the rule'sconfidence. These default settings can be modified using thefollowing options:-N Specify required number of rules -C Specify minimum confidence of a rule -D Specify delta for decrease in minimum support -M Specify lower bound for minimum support -I if set the item sets found are also output (default = no) -T sort examples by different metrics described below: confidence (0) the default, Lift (1), Leverage (2), Conviction (3)Rules can be sorted according to different metrics. This is specifiedusing the -T option. Suppose we have the rule L R and p(X) is the proportion of instances covered by the terms inX. We shall express the various metrics using R, L and p.Lift indicates the degree to which the rule improves the accuracy of the default prediction of its RHS. Lift is confidence divided by the proportion of all examples that are covered by the RHS;i.e.