There's quite a good description of WEKA and its capabilities on the
course page for a module I took this year:
http://www.inf.ed.ac.uk/teaching/courses/dme/html/software2.html
It's more a general suite of data-mining tools rather than a tool to
address a specific task like Taste (plus it's obviously not implemented
for parallel processing which could be problematic for scaling up). From
the link above:
* *Advantages*: The obvious advantage of a package like Weka is that
*a whole range of data preparation, feature selection and data
mining algorithms are integrated*. This means that only one data
format is needed, and trying out and comparing different
approaches becomes really easy. The package also comes with *a
GUI*, which should make it easier to use.
* *Disadvantages*: Probably the most important disadvantage of data
mining suites like this is that *they do not implement the newest
techniques*. For example the MLP implemented has a very basic
training algorithm (backprop with momentum), and the SVM only uses
polynomial kernels, and does not support numeric estimation. ...
*A third possible problem is scaling*. For difficult tasks on
large datasets, the running time can become quite long, and java
sometimes gives an OutOfMemory error. This problem can be reduced
by using the '-mx/x/' option when calling java, where /x/ is
memory size (eg '50m'). For large datasets it will always be
necessary to reduce the size to be able to work within reasonable
time limits. A fourth problem is that *the GUI does not implement
all the possible options*. Things that could be very useful, like
scoring of a test set, are not provided in the GUI, but can be
called from the command line interface. So sometimes it will be
necessary to switch between GUI and command line. Finally, *the
data preparation and visualisation techniques offered might not be
enough*. Most of them are very useful, but I think in most data
mining tasks you will need more to get to know the data well and
to get it in the right format.
Hope that's helpful :-)
Satish Dandu wrote:
Hi,
Recently i started using Taste. It's easy to set up and it really
looks good in terms of picking recommendation (demo using Group lens
dataset for Netflix data). I also went through weka, now my question is
there any difference between WEKA and Taste (as both are open source
machine learning softwares). What advantages can we get by using Taste
(in addition to hadoop integration)
Thanks
