Course overview Introduction to summarization Lecture 1.
Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Chapter Four Basic techniques for cluster.
Data Mining Techniques and Applications, 1 st edition Hongbo Du ISBN 978-1-84480-891-5 © 2010 Cengage Learning Chapter Three Data, pre-processing and.
AI Practice 05 / 07 Sang-Woo Lee. 1.Usage of SVM and Decision Tree in Weka 2.Amplification about Final Project Spec 3.SVM – State of the Art in Classification.
II. Visualization. Data Visualization Data visualization A graphical, animation, or video presentati on of data and the results of data analysis –The.
Berendt: Knowledge and the Web, 2014, berendt/teaching/ 1 Knowledge and the Web – Exploring your data and testing your hypotheses:
Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.
WEKA 3.5.5 (sumber: Machine Learning with WEKA). What is WEKA? Weka is a collection of machine learning algorithms for data mining tasks. Weka contains.
Modeling Other Speaker State COMS 4995/6998 Julia Hirschberg Thanks to William Wang.
Three kinds of learning Supervised learning Learning some mapping from inputs to outputs Unsupervised learning Given “data”, what kinds of patterns can.
An Extended Introduction to WEKA. Data Mining Process.
1 I256: Applied Natural Language Processing Marti Hearst Nov 8, 2006.