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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.
B. Minaei-Bidgoli, R. Barmaki, M. Nasiri
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On the Potential of Numerical Association Rule Mining
2020In association rule mining, both the classical algorithms and today’s available tools either use binary data items or discretized data. However, in real-world scenarios, data are available in many different forms (numerical, text) and these types of data items are not supported in the classical association rule mining algorithms.
Minakshi Kaushik +5 more
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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.
Habiba Drias +2 more
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Mining optimized association rules with categorical and numeric attributes
Proceedings 14th International Conference on Data Engineering, 2002Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes.
R. Rastogi, null Kyuseok Shim
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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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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, Zhibin Liu, Naohiro Ishii
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Analyzing patterns of numerously occurring heart diseases using association rule mining
2017 Twelfth International Conference on Digital Information Management (ICDIM), 2017The use of technology and science in Healthcare has made services available to all the people along with ensuring the best care for the people. Data Mining provides us such useful techniques, which can help the medical practitioners to effectively analyze and discover large amount of data in a more efficient and convenient way as now electronic ...
K.M. Mehedi Hasan Sonet +4 more
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Sensitivity Analysis of MODENAR Method for Mining of Numeric Association Rules
2019 1st International Informatics and Software Engineering Conference (UBMYK), 2019There are many association rules mining studies that focus on datasets consisting of only discrete or binary valued attributes. However, the data in many real-world applications are generally composed of quantitative or numeric values and classical association rule mining methods do not automatically work without preprocessing that disrupt the real ...
Elif Varol Altay, Bilal Alatas
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MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules
Applied Soft Computing, 2008In this paper, a Pareto-based multi-objective differential evolution (DE) algorithm is proposed as a search strategy for mining accurate and comprehensible numeric association rules (ARs) which are optimal in the wider sense that no other rules are superior to them when all objectives are simultaneously considered.
Bilal Alatas, Erhan Akin, Ali Karci
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Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications, 2014In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality ...
Vahid Beiranvand +2 more
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