@inproceedings{24847b59cfa542fa8d02a496c5cae7a3,
title = "Mining frequent patterns from correlated incomplete databases",
abstract = "Modern real-world applications are forced to deal with inconsistent, unreliable and imprecise information. In this setting, considerable research efforts have been put into the field of caring for the intrinsic imprecision of the data. Indeed, several frameworks have been introduced to deal with imperfection such as probabilistic, fuzzy, possibilistic and evidential databases. In this paper, we present an alternative framework, called correlated incomplete database, to deal with information suffering with imprecision. In addition, correlated incomplete database is studied from a data mining point of view. Since, frequent itemset mining is one of the most fundamental problems in data mining, we propose an algorithm to extract frequent patterns from correlated incomplete databases. Our experiments demonstrate the effectiveness and scalability of our framework.",
keywords = "Correlated incomplete database, Evidential database, Frequent itemset mining, Imperfection",
author = "Badran Raddaoui and Ahmed Samet",
note = "Publisher Copyright: Copyright {\textcopyright} 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 8th International Conference on Agents and Artificial Intelligence, ICAART 2016 ; Conference date: 24-02-2016 Through 26-02-2016",
year = "2016",
month = jan,
day = "1",
language = "English",
series = "ICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence",
publisher = "SciTePress",
pages = "377--384",
editor = "Joaquim Filipe and Joaquim Filipe and \{van den Herik\}, Jaap",
booktitle = "ICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence",
}