Expanding the meaning of and applications for data mining

Smith, Marcellus; Pedrycz, Witold

doi:10.1109/icsmc.2000.886384

Cited by 3 publications

(1 citation statement)

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“…Several typical applications are generally divided into three application areas: fraud detection, marketing/customer profiling and network fault isolation [1] [2]. But in recent years due to the rapid increase in the mobile phone user, one more application area for data mining has emerged.…”

Section: Introductionmentioning

confidence: 99%

A Proposal for the Management of Mobile Network's Quality of Service (QoS) using Data Mining Methods

Bhatia¹,

Singh²,

Kumar³

2007

2007 IFIP International Conference on Wireless and Optical Communications Networks

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Today, the challenge for the service operators is not only to attract and subscribe new users but to retain already subscribed users. To gain a competitive edge over other service operators, the operating personnel have to measure the services provided to their users and the network performance in terms of Quality of Service (QoS) at regular periods. By analyzing the information in these measurements, they can manage the quality of service, which helps to improve their service and network performance.

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Section: Introductionmentioning

confidence: 99%

A Proposal for the Management of Mobile Network's Quality of Service (QoS) using Data Mining Methods

Bhatia¹,

Singh²,

Kumar³

2007

2007 IFIP International Conference on Wireless and Optical Communications Networks

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Using Data Mining to Estimate Equivalent Surface of Broadcasting Effect

Liu

Wang

et al. 2009

2009 International Joint Conference on Computational Sciences and Optimization

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The Application of Data Mining to Explore Association Rules between Metabolic Syndrome and Lifestyles

Huang

2013

HIM J

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This study used an efficient data mining algorithm, called DCIP (the data cutting and inner product method), to explore association rules between the lifestyles of factory workers in Taiwan and the metabolic syndrome. A total of 1,216 workers in four companies completed a lifestyle questionnaire. Results of the questionnaire survey were integrated into the workers' health examination reports to form an attribute database of the metabolic syndrome. Among the association rules derived by DCIP, 80% of those on the list of the top 15 highest support counts are corroborated by medical literature or by healthcare professionals. These findings prove that data mining is a valid and effective research method, and that larger sample sizes will likely produce more accurate associations connecting the metabolic syndrome to specific lifestyles. The rules already verified can serve as a reference guide for the health management of factory workers. The remaining 20%, while still lacking hard evidence, provide fertile ground for future research.

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Expanding the meaning of and applications for data mining

Cited by 3 publications

References 0 publications

A Proposal for the Management of Mobile Network's Quality of Service (QoS) using Data Mining Methods

A Proposal for the Management of Mobile Network's Quality of Service (QoS) using Data Mining Methods

Using Data Mining to Estimate Equivalent Surface of Broadcasting Effect

The Application of Data Mining to Explore Association Rules between Metabolic Syndrome and Lifestyles

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