Multitask-based association rule mining
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Tarih
2020
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Recently, there has been a growing interest in association rule mining (ARM) in various fields. However,standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigationand, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novelalgorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multipletasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of theproposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then theselocal rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experimentswere conducted on four different real-world multitask learning datasets. The experimental results indicated that theproposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly consideringthe relationships among multiple tasks.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yapay Zeka, Bilgisayar Bilimleri, Sibernitik, Bilgisayar Bilimleri, Donanım ve Mimari, Bilgisayar Bilimleri, Bilgi Sistemleri, Bilgisayar Bilimleri, Yazılım Mühendisliği, Bilgisayar Bilimleri, Teori ve Metotlar, Mühendislik, Elektrik ve Elektronik, Association rule mining, multitask learning, data mining, the frequent pattern (FP)-Growth algorithm, Parallel, Algorithms, Classification
