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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