Title :
Outlier Detection Using Inductive Logic Programming
Author :
Angiulli, Fabrizio ; Fassetti, Fabio
Author_Institution :
DEIS, Univ. of Calabria, Rende, Italy
Abstract :
We present a novel definition of outlier in the context of inductive logic programming. Given a set of positive and negative examples, the definition aims at singling out the examples showing anomalous behavior. We note that the task here pursued is different from noise removal, and, in fact, the anomalous observations we discover are different in nature from noisy ones. We discuss pecularities of the novel approach, present an algorithm for detecting outliers, discuss some examples of knowledge mined, and compare it with alternative approaches.
Keywords :
inductive logic programming; security of data; anomalous observations; inductive logic programming; noise removal; outlier detection; Data mining; Encoding; Knowledge representation; Learning systems; Logic programming; Machine learning; Supervised learning; Inductive Logic Programming; Outlier detection;
Conference_Titel :
Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5242-2
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2009.127
