Results 201 to 210 of about 2,974 (234)
Some of the next articles are maybe not open access.
Improving Association Rule Mining Using Clustering-based Discretization of Numerical Data
2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), 2018Association rule mining is an important data mining technique that help discover interesting attribute relationships that are useful for decision making. Most association rule mining methods use item-set manipulation approach, whereby data type must be categorical in nature. When a dataset contains numerical attributes, they will need to be discretized
openaire +1 more source
2014 9th International Conference on Industrial and Information Systems (ICIIS), 2014
It has been observed that sometimes in Numeric Association Rule Mining (NARM), it is important to understand the association between a numeric attribute and a specific categorical consequent class attribute. NARM divides the domain of numeric attributes sub-domains without considering particular categorical consequent class attribute.
Pradeep Kumar Saini +2 more
openaire +1 more source
It has been observed that sometimes in Numeric Association Rule Mining (NARM), it is important to understand the association between a numeric attribute and a specific categorical consequent class attribute. NARM divides the domain of numeric attributes sub-domains without considering particular categorical consequent class attribute.
Pradeep Kumar Saini +2 more
openaire +1 more source
Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values
2020Since its introduction in the 1990s, association rule mining(ARM) has been proven as one of the essential concepts in data mining; both in practice as well as in research. Discretization is the only means to deal with numeric target column in today’s association rule mining tools.
Sijo Arakkal Peious +4 more
openaire +1 more source
Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes
2018Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies.
Iztok Fister +5 more
openaire +1 more source
Journal of Ambient Intelligence and Humanized Computing, 2019
Association rules mining (ARM) is one of the most popular tasks of data mining. Although there are many effective algorithms run on binary or discrete-valued data for the problem of ARM, these algorithms cannot run efficiently on data that have numeric-valued attributes.
Elif Varol Altay, Bilal Alatas
openaire +1 more source
Association rules mining (ARM) is one of the most popular tasks of data mining. Although there are many effective algorithms run on binary or discrete-valued data for the problem of ARM, these algorithms cannot run efficiently on data that have numeric-valued attributes.
Elif Varol Altay, Bilal Alatas
openaire +1 more source
Neural Computing and Applications, 2017
The most challenging issues in association rule mining are dealing with numerical attributes and accommodating several criteria to discover optimal rules without any preprocessing activities or predefined parameter values. In order to deal with these problems, this paper proposes a multi-objective particle swarm optimization using an adaptive archive ...
Kuo, R.J., Gosumolo, M., Zulvia, F.E.
openaire +2 more sources
The most challenging issues in association rule mining are dealing with numerical attributes and accommodating several criteria to discover optimal rules without any preprocessing activities or predefined parameter values. In order to deal with these problems, this paper proposes a multi-objective particle swarm optimization using an adaptive archive ...
Kuo, R.J., Gosumolo, M., Zulvia, F.E.
openaire +2 more sources
An Adaptive Method of Numerical Attribute Merging for Quantitative Association Rule Mining
1999Mining quantitative association rules is an important topic of data mining since most real world databases have both numerical and categorical attributes. Typical solutions involve partitioning each numerical attribute into a set of disjoint intervals, interpreting each interval as an item, and applying standard boolean association rule mining ...
Jiuyong Li, Hong Shen, Rodney Topor
openaire +1 more source
Time Series Numerical Association Rule Mining for assisting Smart Agriculture
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022Iztok Fister, Sancho Salcedo-Sanz
openaire +1 more source
2017
In recent years, many new applications, such as location-based services, sensor monitoring systems, and data integration, have shown a growing amount of importance of uncertain data mining. In addition, due to instrument errors, imprecise of sensor monitoring systems, and so on, real-world data tend to be numerical data with inherent uncertainty. Thus,
Bin Pei, Fenmei Wang, Xiuzhen Wang
openaire +1 more source
In recent years, many new applications, such as location-based services, sensor monitoring systems, and data integration, have shown a growing amount of importance of uncertain data mining. In addition, due to instrument errors, imprecise of sensor monitoring systems, and so on, real-world data tend to be numerical data with inherent uncertainty. Thus,
Bin Pei, Fenmei Wang, Xiuzhen Wang
openaire +1 more source
Association Rules Mining based on Numeric Constraint
INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences, 2012Yan Hai -, Zhang Tietou -
openaire +1 more source