An Introduction to the WEKA Data Mining System
Central Connecticut State University
University of Hartford
"Drowning in Data yet Starving for Knowledge"
"Computers have promised us a fountain of wisdom but delivered a flood of data"
William J. Frawley, Gregory Piatetsky-Shapiro, and Christopher J. Matheus
Data Mining: "The non trivial extraction of implicit, previously unknown, and potentially
useful information from data"
William J Frawley, Gregory Piatetsky-Shapiro and Christopher J Matheus
Data mining finds valuable information hidden in large volumes of data.
Data mining is the analysis of data and the use of software techniques for finding
patterns and regularities in sets of data.
Data Mining is an interdisciplinary field involving:
– Machine Learning
– High Performance Computing
KDnuggets : Polls : Data Mining Tools You Used in
2005 (May 2005) PollData mining/Analytic tools you
used in 2005 [376 voters, 860 votes total]
• Enterprise-level: (US $10,000 and more)
Fair Isaac, IBM, Insightful, KXEN, Oracle, SAS, and
• Department-level: (from $1,000 to $9,999)
Angoss, CART/MARS/TreeNet/Random Forests,
Equbits, GhostMiner, Gornik, Mineset, MATLAB,
Megaputer, Microsoft SQL Server, Statsoft Statistica,
• Personal-level: (from $1 to $999): Excel, See5
• Free: C4.5, R, Weka, Xelopes
Data Mining Software
KDnuggets : News : 2005 : n13 : item2
SIGKDD Service Award is the highest service award in the field of data mining and knowledge discovery. It is is given
to one individual or one group who has performed significant service to the data mining and knowledge discovery
field, including professional volunteer services in disseminating technical information to the field, education, and
The 2005 ACM SIGKDD Service Award is presented to the Weka team for their development of the freely-available
Weka Data Mining Software, including the accompanying book Data Mining: Practical Machine Learning Tools and
Techniques (now in second edition) and much other documentation.
The Weka team includes Ian H. Witten and Eibe Frank, and the following major contributors (in alphabetical order of
last names): Remco R. Bouckaert, John G. Cleary, Sally Jo Cunningham, Andrew Donkin, Dale Fletcher, Steve
Garner, Mark A. Hall, Geoffrey Holmes, Matt Humphrey, Lyn Hunt, Stuart Inglis, Ashraf M. Kibriya, Richard
Kirkby, Brent Martin, Bob McQueen, Craig G. Nevill-Manning, Bernhard Pfahringer, Peter Reutemann, Gabi
Schmidberger, Lloyd A. Smith, Tony C. Smith, Kai Ming Ting, Leonard E. Trigg, Yong Wang, Malcolm Ware, and
The Weka team has put a tremendous amount of effort into continuously developing and maintaining the system since
1994. The development of Weka was funded by a grant from the New Zealand Government's Foundation for
Research, Science and Technology.
The key features responsible for Weka's success are:
– it provides many different algorithms for data mining and machine learning
– is is open source and freely available
– it is platform-independent
– it is easily useable by people who are not data mining specialists
– it provides flexible facilities for scripting experiments
–it has kept up-to-date, with new algorithms being added as they appear in the research literature.
Weka Data Mining Software
Weka Data Mining Software
KDnuggets : News : 2005 : n13 : item2 (cont.)
The Weka Data Mining Software has been downloaded 200,000 times since it was put on SourceForge in April
2000, and is currently downloaded at a rate of 10,000/month. The Weka mailing list has over 1100
subscribers in 50 countries, including subscribers from many major companies.
There are 15 well-documented substantial projects that incorporate, wrap or extend Weka, and no doubt many
more that have not been reported on Sourceforge.
Ian H. Witten and Eibe Frank also wrote a very popular book "Data Mining: Practical Machine Learning
Tools and Techniques" (now in the second edition), that seamlessly integrates Weka system into teaching
of data mining and machine learning. In addition, they provided excellent teaching material on the book
This book became one of the most popular textbooks for data mining and machine learning, and is very
frequently cited in scientific publications.
Weka is a landmark system in the history of the data mining and machine learning research communities,
because it is the only toolkit that has gained such widespread adoption and survived for an extended period
of time (the first version of Weka was released 11 years ago). Other data mining and machine learning
systems that have achieved this are individual systems, such as C4.5, not toolkits.
Since Weka is freely available for download and offers many powerful features (sometimes not found in
commercial data mining software), it has become one of the most widely used data mining systems. Weka
also became one of the favorite vehicles for data mining research and helped to advance it by making many
powerful features available to all.
In sum, the Weka team has made an outstanding contribution to the data mining field.
Model evaluation – leave one out cross validation
Model evaluation – confusion (contingency) matrix
Clustering – k-means
Click on Ignore attributes
Hierarchical Clustering – Cobweb
Association Rules (A => B)
• Confidence (accuracy): P(B|A) = (# of tuples containing both A and B) / (# of tuples containing A).
• Support (coverage): P(A,B) = (# of tuples containing both A and B) / (total # of tuples)
And many more …