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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Book ChapterDOI
Fast Outlier Detection in High Dimensional Spaces
Fabrizio Angiulli,Clara Pizzuti +1 more
TL;DR: A new definition of distance-based outlier that considers for each point the sum of the distances from its k nearest neighbors, called weight, is proposed, which scales linearly both in the dimensionality and the size of the data set.
Journal ArticleDOI
Outlier mining in large high-dimensional data sets
Fabrizio Angiulli,Clara Pizzuti +1 more
TL;DR: An in-memory and disk-based implementation of the HilOut algorithm and a thorough scaling analysis for real and synthetic data sets showing that the algorithm scales well in both cases are presented.
Journal ArticleDOI
Distance-based detection and prediction of outliers
TL;DR: A distance-based outlier detection method that finds the top outliers in an unlabeled data set and provides a subset of it that can be used to predict the outlierness of new unseen objects is proposed.
Proceedings ArticleDOI
Detecting distance-based outliers in streams of data
Fabrizio Angiulli,Fabio Fassetti +1 more
TL;DR: In this work a method for detecting distance-based outliers in data streams is presented, where outlier queries are performed in order to detect anomalies in the current window using the sliding window model.
Journal ArticleDOI
Fast Nearest Neighbor Condensation for Large Data Sets Classification
TL;DR: The fast condensed nearest neighbor (FCNN) rule was three orders of magnitude faster than hybrid instance-based learning algorithms on the MNIST and Massachusetts Institute of Technology Face databases and computed a model of accuracy comparable to that of methods incorporating a noise-filtering pass.