background preloader

WEKA

Facebook Twitter

Datasets

Missing Values. Decision Trees. Bayes. KNN. ECT 584 - Data Mining in Weka. Analyzing Categorical Data: Data files. CSC 578 course home page. ECT 584 - Web Data Mining - DePaul University. Condor.depaul.edu/ntomuro/courses/578/assign/hw1files/halloffame.arff. Mputational Intelligence for Technology Enhanced Learning - Fatos Xhafa, Ajith Abraham, Santi Caball. E-Learning has become one of the most wide spread ways of distance teaching and learning.

mputational Intelligence for Technology Enhanced Learning - Fatos Xhafa, Ajith Abraham, Santi Caball

Technologies such as Web, Grid, and Mobile and Wireless networks are pushing teaching and learning communities to find new and intelligent ways of using these technologies to enhance teaching and learning activities. Indeed, these new technologies can play an important role in increasing the support to teachers and learners, to shorten the time to learning and teaching; yet, it is necessary to use intelligent techniques to take advantage of these new technologies to achieve the desired support to teachers and learners and enhance learners’ performance in distributed learning environments. The chapters of this volume bring advances in using intelligent techniques for technology enhanced learning as well as development of e-Learning applications based on such techniques and supported by technology. RuleQuest Research Data Mining Tools. Imputation. Partitioning Data into Training and Testing Sets (Analysis Services - Data Mining) Separating data into training and testing sets is an important part of evaluating data mining models.

Partitioning Data into Training and Testing Sets (Analysis Services - Data Mining)

Typically, when you separate a data set into a training set and testing set, most of the data is used for training, and a smaller portion of the data is used for testing. Analysis Services randomly samples the data to help ensure that the testing and training sets are similar. By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model. After a model has been processed by using the training set, you test the model by making predictions against the test set.

Because the data in the testing set already contains known values for the attribute that you want to predict, it is easy to determine whether the model's guesses are correct. In SQL Server 2012, you separate the original data set at the level of the mining structure. Using the Data Mining Wizard to Divide a Mining Structure. Undo discretization in weka. Datamining2 : Data Mining Club - 1900+ members!! Www.csie.ntu.edu.tw/~p88012/AI-final.pdf. Www.rorylewis.com/PDFs/03UCCS/CS450/weka tutorial 2.pdf. CiteSeerX A Support Vector Machine Approach to Decision Trees. BibTeX @INPROCEEDINGS{Bennett97asupport, author = {K.

CiteSeerX A Support Vector Machine Approach to Decision Trees

P. Bennett and J.A. Blue}, title = {A Support Vector Machine Approach to Decision Trees}, booktitle = {Department of Mathematical Sciences Math Report No. 97-100, Rensselaer Polytechnic Institute}, year = {1997}, pages = {2396--2401}} Years of Citing Articles Bookmark OpenURL Abstract Key ideas from statistical learning theory and support vector machines are generalized to decision trees. Pdf/0708.4274.pdf. Statistical symbols & probability symbols (,,...) Hinf6210 Project Classification Of Breast Cancer Dataset. Weka-tutorial. Data mining with WEKA, Part 2: Classification and clustering. Introduction In Part 1, I introduced the concept of data mining and to the free and open source software Waikato Environment for Knowledge Analysis (WEKA), which allows you to mine your own data for trends and patterns.

Data mining with WEKA, Part 2: Classification and clustering

I also talked about the first method of data mining — regression — which allows you to predict a numerical value for a given set of input values. This method of analysis is the easiest to perform and the least powerful method of data mining, but it served a good purpose as an introduction to WEKA and provided a good example of how raw data can be transformed into meaningful information. In this article, I will take you through two additional data mining methods that are slightly more complex than a regression model, but more powerful in their respective goals. Where a regression model could only give you a numerical output with specific inputs, these additional models allow you to interpret your data differently.

Back to top Regression Classification Clustering Classification.