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Multi-objective bat algorithm for mining numerical association rules
International Journal of Bio-Inspired Computation, 2018Numerical association rule mining problem attracts the attention of researchers because of the various applications and its importance in our world with the fast growth of the stored data. ARM is computationally very expensive because the number of rules grows exponentially as the number of items in the database increases.
Kamel Eddine Heraguemi +2 more
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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
semanticscholar +2 more sources
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
semanticscholar +2 more sources
Intelligent optimization algorithms for the problem of mining numerical association rules
Physica A: Statistical Mechanics and its Applications, 2020Abstract There are many effective approaches that have been proposed for association rules mining (ARM) on binary or discrete-valued data. However, in many real-world applications, the data usually consist of numerical values and the standard algorithms cannot work or give promising results on these datasets.
Elif Varol Altay, Bilal Alatas
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International Symposium on Computer Science and Intelligent Control, 2023
Frequent item-sets are sparse in real life transaction datasets, and data prerecession including data augmentation skills are necessary in order to carry out efficient mining. For example, the raw data (e.g. Power Mart transaction data set) contained the
F. Chan, Cheuk-Yin Cheung, Bingjie Ni
semanticscholar +1 more source
Frequent item-sets are sparse in real life transaction datasets, and data prerecession including data augmentation skills are necessary in order to carry out efficient mining. For example, the raw data (e.g. Power Mart transaction data set) contained the
F. Chan, Cheuk-Yin Cheung, Bingjie Ni
semanticscholar +1 more source
An Adaptive Method of Numerical Attribute Merging for Quantitative Association Rule Mining
Lecture Notes in Computer Science, 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, Rodney Topor
exaly +2 more sources
Mining Association Rules on Related Numeric Attributes
1999In practical applications, some property is represented by a pair of related attributes. For example, blood pressure, temperature changes etc. The existing data mining approaches for association rules can not tackle those cases, because they treat every attribute independently.
Xiaoyong Du 0001 +2 more
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Wolf search algorithm for numeric association rule mining
2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 2016Big data has become one of the key sources for valuable information and as information becomes larger it poses some computational challenge in finding a best possible solution for mining association rules and discovering patterns in data. Meta-heuristic algorithm when applied to mining association rules aims to find best possible rules from data ...
Israel Edem Agbehadji +2 more
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Mining numerical association rules via multi-objective genetic algorithms
Information Sciences, 2013Association rule discovery is an ever increasing area of interest in data mining. Finding rules for attributes with numerical values is still a challenging point in the process of association rule discovery. Most of popular methods for association rule mining cannot be applied to the numerical data without data discretization.
Behrouz Minaei-Bidgoli +2 more
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Shapelet-based Temporal Association Rule Mining for Multivariate Time Series Classification
2022 IEEE International Conference on Big Data (Big Data), 2022The rapid upsurge of numerical sources of information and the growth of storage capacities in recent years has resulted in the collection of massive time series datasets.
O. Bahri +3 more
semanticscholar +1 more source
Concurrency and Computation, 2021
In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability.
E. Altay, Bilal Alatas
semanticscholar +1 more source
In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability.
E. Altay, Bilal Alatas
semanticscholar +1 more source