F
Fabrizio Angiulli
Researcher at University of Calabria
Publications - 130
Citations - 3268
Fabrizio Angiulli is an academic researcher from University of Calabria. The author has contributed to research in topics: Anomaly detection & Outlier. The author has an hindex of 22, co-authored 120 publications receiving 2890 citations. Previous affiliations of Fabrizio Angiulli include Indian Council of Agricultural Research & National Research Council.
Papers
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Journal ArticleDOI
Outlier detection by logic programming
TL;DR: In this article, the concept of outlier is formally stated in the context of knowledge-based systems, by generalizing that originally proposed in Angiulli et al. [2003] and a rewriting algorithm is proposed that transforms any outlier detection problem into an equivalent inference problem under stable model semantics, thereby making outlier computation effective and realizable on top of any stable model solver.
Journal ArticleDOI
Condensed Nearest Neighbor Data Domain Description
TL;DR: This work investigates the effect of using a subset of the original data set as the reference set of the classifier, and introduces the concept of a reference consistent subset, and shows that finding the minimum cardinalityreference consistent subset is intractable.
Book ChapterDOI
Gene expression biclustering using random walk strategies
Fabrizio Angiulli,Clara Pizzuti +1 more
TL;DR: A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed and experimental results show that the method is able to find significant biclusters.
Journal ArticleDOI
Exploiting domain knowledge to detect outliers
Fabrizio Angiulli,Fabio Fassetti +1 more
TL;DR: A novel definition of outlier is presented whose aim is to embed an available domain knowledge in the process of discovering outliers, and a notion of compliance of a set of facts with respect to a background knowledge and aSet of examples is exploited to detect the examples that prevent to improve generalization of the induced hypothesis.
Book ChapterDOI
A distributed approach to detect outliers in very large data sets
TL;DR: While solving the distance-based outlier detection task in the distributed scenario, the method computes an outlier Detection solving set of the overall data set ofThe same quality as that computed by the corresponding centralized method.