Actionable Pattern Mining - A Scalable Data Distribution Method Based on Information Granules

Arunkumar Bagavathi , Abhishek Tripathi , Angelina A. Tzacheva , Zbigniew W. Raś


Actionable patterns are a form of recommendations that gives knowledge required by the user on actions they need to undertake to attain profit or achieve goals. Action rule is one of the methods to mine actionable patterns that are hidden in a large dataset. In the modern era of big data, organizations are collecting massive amounts of data and they keep the data updated constantly, including in major domains like financial, medical, as well as social media and IoT. Classical action rules extraction models, which analyze the data in a non-distributed fashion, lacks efficiency when processing big volumes of data and thus require a distributed approach to extract rules. Major complications of discovering action rules with distributed methods include data distribution among processing nodes in the cluster and extracting mined rule parameters like: support, confidence, utility, and coverage, representing the entire data. In this work, we focus on data distribution phase of the distributed actionable pattern mining problem. Very few works have been proposed to distribute the big data in both horizontal and vertical fashions, and extract rules/knowledge from the distributed data. We primarily focus on the vertical data splitting strategy using information granules, which give meaningful representations of complex information systems as fine grained granules. We tend to make the vertical data partitioning more logical using information granules instead of splitting the data in random.
Author Arunkumar Bagavathi - [The University of North Carolina at Charlotte]
Arunkumar Bagavathi,,
, Abhishek Tripathi - [The University of North Carolina at Charlotte]
Abhishek Tripathi,,
, Angelina A. Tzacheva - [University of North Carolina at Charlotte (UNC Charlotte)]
Angelina A. Tzacheva,,
, Zbigniew W. Raś (FEIT / IN)
Zbigniew W. Raś,,
- The Institute of Computer Science
Publication size in sheets0.5
Book Wani M. Arif, Kantardzic Mehmed, Sayed-Mouchaweh Moamar , Gama Joao , Lughofer Edwin (eds.): Proceedings 17th IEEE International Conference on Machine Learning and Applications ICMLA 2018 , 2018, Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, Institute of Electrical and Electronics Engineers, ISBN 978-1-5386-6804-7, 1495 p.
Keywords in EnglishAction Rules, Information granules, Vertical data distribution, Spark
ProjectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław, , Phone: +48 22 234 7432, start date 01-08-2018, end date 30-09-2019, II/2018/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
Z__Ras_actionable pattern.pdf 284.97 KB
Score (nominal)20
Score sourceconferenceList
ScoreMinisterial score = 20.0, 01-02-2020, ChapterFromConference
Publication indicators Scopus Citations = 2; WoS Citations = 0
Citation count*4 (2020-09-16)
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