What is new in ELKI 0.7.0? Too much, see the
What is ELKI exactly?
ELKI is a Java based data mining toolkit. We focus on cluster analysis
and outlier detection, because there are plenty of tools available for
classification already. But there is a kNN classifier, and a number of frequent
itemset mining algorithms in ELKI, too.
ELKI is highly modular. You can combine almost everything
with almost everything else. In particular, you can combine algorithms such
as DBSCAN, with arbitrary distance functions, and you can choose from
many index structures to accelerate the algorithm. But because we
separate them well, you can add a new index, or a new distance function,
or a new data type, and still benefit from the other parts.
In other tools such as R, you cannot easily add a new distance function
into an arbitrary algorithm and get good performance - all the fast code
in R is written in C and Fortran; and cannot
be easily extended this way. In ELKI, you can define a new data type, new
distance function, new index, and still use most algorithms. (Some algorithms
may have prerequisites that e.g. your new data type does not fulfill, of
ELKI is also very fast. Of course a good C code can be faster - but then
it usually is not as modular and easy to extend anymore.
ELKI is documented. We have JavaDoc, and we annotate classes with their
(see a list of all references we have
). So you know which algorithm a class is supposed
to implement, and can look up details there. This makes it very useful
ELKI is not: a turnkey solution. It aims at researchers, developers and
data scientists. If you have a SQL database, and want to do a point-and-click
analysis of your data, please get a business solution instead with commercial