Vulnerabilities with publically known exploits typically form 2-7% of all vulnerabilities reported for a given software version. With smaller number exploited compared the total vulnerabilities, it is more difficult to model and predict when vulnerability exploit will be reported. In this paper, we introduce an approach predicting discovery pattern using software. Eight commonly used models (VD...

The paper is a comparative study of major modeling and pattern discovery approaches applicable to the area of Bioinformatics and the area of decision support systems in general. These approaches include inductive versus transductive reasoning, global, local, and personalized modeling and their potentials are illustrated on a case study of gene expression and clinical data related to cancer outc...

The word ‘pattern’ frequently appears in the visualisation and visual analytics literature, but what do we mean when talk about patterns? We propose a practicable definition of concept pattern data distribution as combination multiple interrelated elements two or more components that can be represented treated unified whole. Our theoretical model describes how patterns are made by relationships...

Local pattern discovery, pattern set discovery and global modeling build together as consecutive steps a specific case of global pattern discovery. As each of these three steps have gained an increased attention in recent years, a great variety of techniques for each step have been proposed. Though so far there has been no systematic comparison of the possible choices. In this paper, we will co...

In this paper, we consider finding a small set of substring patterns which classifies the given documents well. We formulate the problem as 1 norm soft margin optimization problem where each dimension corresponds to a substring pattern. Then we solve this problem by using LPBoost and an optimal substring discovery algorithm. Since the problem is a linear program, the resulting solution is likel...

We consider a modified version of the local pattern discovery task of subgroup discovery, where subgroups dominated by other subgroups are discarded. The advantage of this modified task, known as relevant subgroup discovery, is that it avoids redundancy in the outcome. Although it was considered in many applications, so far no efficient and exact algorithm for this task has been proposed. One p...