12,725 research outputs found
Parametric matroid of rough set
Rough set is mainly concerned with the approximations of objects through an
equivalence relation on a universe. Matroid is a combinatorial generalization
of linear independence in vector spaces. In this paper, we define a parametric
set family, with any subset of a universe as its parameter, to connect rough
sets and matroids. On the one hand, for a universe and an equivalence relation
on the universe, a parametric set family is defined through the lower
approximation operator. This parametric set family is proved to satisfy the
independent set axiom of matroids, therefore it can generate a matroid, called
a parametric matroid of the rough set. Three equivalent representations of the
parametric set family are obtained. Moreover, the parametric matroid of the
rough set is proved to be the direct sum of a partition-circuit matroid and a
free matroid. On the other hand, since partition-circuit matroids were well
studied through the lower approximation number, we use it to investigate the
parametric matroid of the rough set. Several characteristics of the parametric
matroid of the rough set, such as independent sets, bases, circuits, the rank
function and the closure operator, are expressed by the lower approximation
number.Comment: 15 page
Soft data mining, computational theory of perceptions, and rough-fuzzy approach
Data mining and knowledge discovery is described from pattern recognition point of view along with the relevance of soft computing. Key features of the computational theory of perceptions and its significance in pattern recognition and knowledge discovery problems are explained. Role of fuzzy-granulation (f-granulation) in machine and human intelligence, and its modeling through rough-fuzzy integration are discussed. Merits of fuzzy granular computation, in terms of performance and computation time, for the task of case generation in large scale case-based reasoning systems are illustrated through an example
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information
and Knowledge Management (CIKM 2018
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