An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects
Emerging pattern mining is a data mining task that aims to discover discriminative patterns,
which can describe emerging behavior with respect to a property of interest. In recent years …
which can describe emerging behavior with respect to a property of interest. In recent years …
Identifying key factors of student academic performance by subgroup discovery
Identifying the factors that influence student academic performance is essential to provide
timely and effective support interventions. The data collected during enrolment and after …
timely and effective support interventions. The data collected during enrolment and after …
Towards the significance of taxi recommender systems in smart cities
R Katarya - Concurrency and Computation: Practice and …, 2023 - Wiley Online Library
Since their launch in the early 1990's, recommender systems (RSs) have played an
essential role in information filtering and providing personalized information to users by …
essential role in information filtering and providing personalized information to users by …
A view on fuzzy systems for big data: progress and opportunities
Currently, we are witnessing a growing trend in the study and application of problems in the
framework of Big Data. This is mainly due to the great advantages which come from the …
framework of Big Data. This is mainly due to the great advantages which come from the …
Subgroup discovery algorithms: a survey and empirical evaluation
S Helal - Journal of computer science and technology, 2016 - Springer
Subgroup discovery is a data mining technique that discovers interesting associations
among different variables with respect to a property of interest. Existing subgroup discovery …
among different variables with respect to a property of interest. Existing subgroup discovery …
Urinary Metabolic Distinction of Niemann–Pick Class 1 Disease through the Use of Subgroup Discovery
In this investigation, we outline the applications of a data mining technique known as
Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based …
Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based …
MEFASD-BD: multi-objective evolutionary fuzzy algorithm for subgroup discovery in big data environments-a mapreduce solution
F Pulgar-Rubio, AJ Rivera-Rivas… - Knowledge-Based …, 2017 - Elsevier
Nowadays, there is an incredible increase of data volumes around the world, with the
Internet as one of the main actors in this scenario and a growth rate above 30GB/s. The …
Internet as one of the main actors in this scenario and a growth rate above 30GB/s. The …
A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy
Supervised descriptive rule discovery represents a set of data mining techniques whose
objective is to describe data with respect to a property of interest. This concept encompasses …
objective is to describe data with respect to a property of interest. This concept encompasses …
Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database
The computer-aided diagnosis has become one of the major research topics in medical
diagnostics. In this research paper, we focus on designing an automated computer …
diagnostics. In this research paper, we focus on designing an automated computer …
Conditional discriminative pattern mining: concepts and algorithms
Z He, F Gu, C Zhao, X Liu, J Wu, J Wang - Information Sciences, 2017 - Elsevier
Discriminative pattern mining is used to discover a set of significant patterns that occur with
disproportionate frequencies in different class-labeled data sets. Although there are many …
disproportionate frequencies in different class-labeled data sets. Although there are many …