weka datasets

/~ Strehl/ 6. Perform text classification WebHttp://www-2.cs.cmu.edu/afs/cs/project/theo-11/www/naive-bayes.html Http://www.w3.org/TR/WD-logfile-960221.htmlHttp://www.w3.org/Daemon/User/Config/Logging.html#AccessLogHttp://www.w3.org/1998/11/05/WC-workshop/Papers/bala2.htmlHttp://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/Http://www.web-caching.com/traces-logs.htmlHttp://www-2.cs.cmu.edu/webkbHttp://www.cs.auc.dk/research/DP/tdb/TimeCenter/TimeCenterPublications/TR-75.pdfHttp://ww

Refer to association Rules algorithm, generally think of Apriori or FP, generally very few think of the hotspot, the algorithm does not know the application is less or I check the data means too low, in the Internet only found very little content, this article http:/ /wiki.pentaho.com/display/datamining/hotspot+segmentation-profiling, probably analyzed a little, the other seems to have not seen how. More useful algorithm class software, such as Weka,

number of datasets and then do a higher-cost clustering analysis for each subclass. K-means can also be used to quickly process "K" and explore whether there are neglected patterns or relationships in the data set.But using the K-means algorithm is not smooth sailing:The two key weaknesses of the K-means algorithm are its sensitivity to outliers and its sensitivity to the initial center point selection. The last one to remember is that the K-means al

distance measure) and perturbing aninstance one attribute at a Time by a random amount within the difference tothe neighboring instances.Learn more on SMOTE, see the original2002 paper titled "Smote:synthetic Minority over-sampling technique".There is anumber of implementations of the SMOTE algorithm, for example: In Python, take alook at the "Unbalanceddataset" module. It provides a number ofimplementations of SMOTE as well as various other resampling techniques thatyou could try.

Original address: Http://www.demnag.com/b/java-machine-learning-tools-libraries-cm570/?ref=dzoneThis is a list of the Java machine learning tools libraries. Weka have a collection of machine learning algorithms for data mining tasks. The algorithms can either is applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clus

for Java applications and supports output to printers and PDFs, Excel, HTML and XHTML, plaintext, XML and CSV files; Eclipse BIRT, an enterprise intelligence and reporting tool under Eclipse, enables the creation of beautiful, eye-catching PDF or HTML-formatted reports for Java EE Web applications, which provides the core reporting capabilities. 2.3 OLAP ToolsOnline analysis processing tool. Currently open source OLAP tools are also divided into MOLAP (multidimensional), ROLAP (relatio

25 Java machine learning tools and libraries It industry more and more fire, with more new troops to join the IT family, the proportion of Java is also more and more large, the following for everyone to organize a number of learning tools. 1. Weka integrates a machine learning algorithm for data mining work. These algorithms can be applied directly to a dataset or you can write your own code to invoke it. Weka