Download viper:an improved visual pattern - Leiden Institute of Advanced

V IPER : AN IMPROVED V ISUAL PATTERN
E XPLORER
W OUTER H UIJS
AND
L EIDEN I NSTITUTE
OF
M ATTHIJS
VAN
L EEUWEN
A DVANCED C OMPUTER S CIENCE
[email protected]
O VERVIEW
R ESEARCH Q UESTION
We present Viper, for Visual Pattern Explorer,
an innovative, browser-based application for
interactive pattern exploration, assisted by visualisation, recommendation, and algorithmic
search. The target audience consists of domain
experts who have access to data but not to, potentially expensive, data mining experts. The
goal of the system is to enable the target audience to perform true exploratory data mining.
That is, to discover interesting patterns from
data, with a focus on subgroup discovery but
also facilitating frequent itemset mining.
Research Question
• Is it possible, for a domain-expert who has no knowledge about data mining, to find interesting patterns in the data, using visual recommendations?
For domain-experts, it is hard to apply data mining, because most of the data mining software
uses to many parameters and algorithms. An another problem is that traditional software shows
very much patterns, not all of which are good.
So, we hope that domain-experts will use data mining with more ease.
V IPER 1.0
In the previous version of VIPER, the visualisation and user-feedback were very important. In
Figure 1 you see the layout of VIPER 1.0.
R ELEVANT W ORK
This research is based on the Master Thesis of
Lara Cardinaels (Cardinaels, Leeuwen, 2015).
Troughout the years several pattern mining
systems with a graphical user interface have
been developed, such as MIME (Goebel,
et al, 2011) for FIM and Cortana (datamining.liacs.nl/cortana.html) for SD. These tools
are generally inaccessible to domain experts,
our target audience, due to the large number
of algorithms, measures, and parameters.
More recently, interestingness measures
have been investigated that can adapt to the
background knowledge and/or feedback of a
user. Bhuiyan (Bhuiyan et al, 2012) proposed
to use user feedback to adapt the sampling
distribution of itemsets.
Boley (Boley et al, 2013) did present a system
for ´one-click-mining´, in which the preferences of the user for certain algorithms and
patterns are learned. Still, objective interestingness measures are used to mine patterns,
which are then presented to the user.
Figure 1: A preview of VIPER (Cardinaels, Leeuwen, 2015)
V IPER 2.0, THE IMPROVED VERSION
F URTHER RESEARCH
In Figure 2 you see the stripped-down version of the UML class diagram of the new Viper. The
system will use the Model-View-Controller design, which means that the algorithms and the
visualisation are several layers.
Given the size of the project, it isn’t possible to
make a system with multiple algorithms and
multiple quality measures. In this project we
will focus on the design of the system (both
the visual and the technical side). Besides that,
are building-style will be user-centered, so,
the design and feedback will be optimised.
In the future, if VIPER is faster and easier for
Domain-experts, VIPER can be optimised and
more algoritmhs and quality measures can be
added. Also, the support can be optimised.
Are the recommendations better when you
calculates two or more steps deep?
Figure 2: The stripped-down version of the UML Class Diagram of the new Viper