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.
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Proceedings ArticleDOI
Fast condensed nearest neighbor rule
TL;DR: This work presents a novel algorithm for computing a training set consistent subset for the nearest neighbor decision rule, and compares it with state of the art competence preservation algorithms on large multidimensional training sets, showing that it outperforms existing methods in terms of learning speed and learning scaling behavior.
Journal ArticleDOI
DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets
Fabrizio Angiulli,Fabio Fassetti +1 more
TL;DR: In this work a novel distance-based outlier detection algorithm, named DOLPHIN, working on disk-resident datasets and whose I/O cost corresponds to the cost of sequentially reading the input dataset file twice, is presented.
Journal ArticleDOI
Distance-based outlier queries in data streams: the novel task and algorithms
Fabrizio Angiulli,Fabio Fassetti +1 more
TL;DR: Experimental results have confirmed the effectiveness of the proposed approach and the good quality of the solutions, and the accuracy properties and memory consumption of the algorithms have been theoretically assessed.
Journal ArticleDOI
Detecting outlying properties of exceptional objects
TL;DR: This article is concerned with the problem of discovering sets of attributes that account for the (a priori stated) abnormality of an individual within a given dataset and proposes efficient algorithms for detecting both global and local forms of most abnormal properties.
Journal ArticleDOI
Distributed Strategies for Mining Outliers in Large Data Sets
TL;DR: A distributed method for detecting distance-based outliers in very large data sets based on the concept of outlier detection solving set, which is a small subset of the data set that can be also employed for predicting novel outliers, which exploits parallel computation in order to obtain vast time savings.