Learning Data Mining with Python

Next Steps…
Extending the IPython Notebook
http://ipython.org/ipython-doc/1/interactive/public_server.html
The IPython Notebook is a powerful tool. It can be extended in many ways, and
one of those is to create a server to run your Notebooks, separately from your main
computer. This is very useful if you use a low-power main computer, such as a small
laptop, but have more powerful computers at your disposal. In addition, you can set
up nodes to perform parallelized computations.More datasets are available at:
http://archive.ics.uci.edu/ml/
There are many datasets available on the Internet, from a number of different
sources. These include academic, commercial, and government datasets. A collection
of well-labelled datasets is available at the UCI ML library, which is one of the best
options to find datasets for testing your algorithms.
Try out the OneR algorithm with some of these different datasets.
Chapter 2 – Classifying with scikit-learn
Estimators
Scalability with the nearest neighbor
https://github.com/jnothman/scikit-learn/tree/pr2532
A naïve implementation of the nearest neighbor algorithm is quite slow—it checks
all pairs of points to find those that are close together. Better implementations exist,
with some implemented in scikit-learn. For instance, a kd-tree can be created that
speeds up the algorithm (and this is already included in scikit-learn).
Another way to speed up this search is to use locality-sensitive hashing,
Locality-Sensitive Hashing (LSH). This is a proposed improvement for scikit-learn,
and hasn't made it into the package at the time of writing. The above link gives a
development branch of scikit-learn that will allow you to test out LSH on a dataset.
Read through the documentation attached to this branch for details on doing this.
[ 298 ]