The use of data mining to classify Carménère and Merlot wines from Chile

2018 ◽  
Vol 36 (2) ◽  
pp. e12361
Author(s):  
Nattane Luíza Costa ◽  
Laura Andrea García Llobodanin ◽  
Inar Alves Castro ◽  
Rommel Barbosa
Keyword(s):  
Data Mining ◽  
Use Of Data

scholarly journals Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

2017 ◽  
Vol 8 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Masoud Al Quhtani
Keyword(s):  
Data Mining ◽  
Information Security ◽  
Intrusion Detection ◽  
Mining System ◽  
Systematic Analysis ◽  
New Methods ◽  
Use Of Data ◽  
Efficient Data ◽  
Abuse Risk ◽  
Corporate Information

AbstractBackground: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

Fault Diagnosis of Automobile ECUs with Data Mining Technologies

2010 ◽  
Vol 40-41 ◽  
pp. 156-161 ◽  
Author(s):  
Yang Li ◽  
Yan Qiang Li ◽  
Zhi Xue Wang
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Data Stream ◽  
Electronic Control Unit ◽  
Rapid Development ◽  
Reliability And Validity ◽  
Control Unit ◽  
Use Of Data ◽  
The Cost

With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

Use of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries

2021 ◽  
Vol 8 (4) ◽  
pp. 638-645
Author(s):  
W. Boutayeb ◽  
◽  
M. Badaoui ◽  
H. Al Ali ◽  
A. Boutayeb ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gulf Coast ◽  
Use Of Data ◽  
Gulf Countries ◽  
Unhealthy Diet

Prevalence of diabetes in Gulf countries is knowing a significant increase because of various risk factors, such as: obesity, unhealthy diet, physical inactivity and smoking. The aim of our proposed study is to use Data Mining and Data Analysis tools in order to determine different risk factors of the development of Type~2 diabetes mellitus (T2DM) in Gulf countries, from Gulf COAST dataset.

scholarly journals Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

2012 ◽  
Vol 32 (1) ◽  
pp. 184-196 ◽  
Author(s):  
Rubens A. C. Lamparelli ◽  
Jerry A. Johann ◽  
Éder R. dos Santos ◽  
Julio C. D. M. Esquerdo ◽  
Jansle V. Rocha
Keyword(s):  
Data Mining ◽  
Expectation Maximization ◽  
Spectral Behavior ◽  
Use Of Data ◽  
Spectral Profiles ◽  
Coffee Plants ◽  
Using Data ◽  
Hyperion Image ◽  
Noise Fraction ◽  
Behavior Profiles

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

A structured model for evaluating countermeasures at highway–railway grade crossings

2005 ◽  
Vol 32 (4) ◽  
pp. 627-635 ◽  
Author(s):  
Young-Jin Park ◽  
Frank F Saccomanno
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Expected Number ◽  
Structured Model ◽  
Support Tool ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Grade Crossings

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highway–railway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highway–railway grade crossing, collision prediction model, countermeasures, Poisson regression.

scholarly journals Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients

2013 ◽  
Vol 19 (2) ◽  
pp. 121 ◽  
Author(s):  
Peyman Rezaei Hachesu ◽  
Maryam Ahmadi ◽  
Somayyeh Alizadeh ◽  
Farahnaz Sadoughi
Keyword(s):  
Data Mining ◽  
Length Of Stay ◽  
Cardiac Patients ◽  
Data Mining Techniques ◽  
Use Of Data

scholarly journals Análise do banco de atendimentos da Defesa Civil do Estado de São Paulo

2010 ◽  
Vol 33 (1) ◽  
pp. 35-43
Author(s):  
Diego José Chagas ◽  
Chou Sin Chan ◽  
Alessandra Cristina Corsi
Keyword(s):  
Data Mining ◽  
Sao Paulo ◽  
Data Organization ◽  
São Paulo ◽  
Data Mining Technique ◽  
Landslide Occurrence ◽  
Serra Do Mar ◽  
Use Of Data ◽  
Relevant Factors

In recent years the simple data organization is no longer a differential factor for institutions, since, depending on their volume, the traditional method of analysis and interpretation is extremely slow and costly. The use of data mining techniques is an alternative to allow this process semi-automatic. The objective of this work is to carry out a case study of data mining technique based on the WEKA software applied to hydrometeorological and geomorphological data which were collected in the Serra do Mar region of São Paulo State. Results obtained from the application of the association technique indicate that the presence of rock and boulders at terrains with scars and high declivity are relevant factors for the landslide occurrence.

scholarly journals KAJIAN PERILAKU BELANJA KONSUMEN MENGGUNAKAN TEKNIK ASOSIASI DI SUPERMARKET (STUDI KASUS: TOSERBA X). [STUDY OF CONSUMER SHOPPING BEHAVIOR USING ASSOCIATION TECHNIQUES IN SUPERMARKET (CASE STUDY: TOSERBA X)]

2020 ◽  
Vol 15 (2) ◽  
pp. 282
Author(s):  
Ronny Samsul Bahri ◽  
Laura Lahindah
Keyword(s):  
Data Mining ◽  
Shopping Behavior ◽  
Mining Method ◽  
Consumer Spending ◽  
Market Basket Analysis ◽  
Association Pattern ◽  
Use Of Data ◽  
Spending Patterns ◽  
Bahasa Indonesia

<p><em>The development of retail companies in Indonesia is quite rapid causing the need for the use of data as a basis for decision making. As one of the developing retail stores, the floor display pattern has not been well managed and has not been linked to the pattern of consumer spending. Market basket analysis is one of the data mining method techniques to determine the association of consumer spending patterns in a purchase transaction. This study aims to determine whether there is an association pattern in each term of consumer spending in five divisions of supermarket products (all divisions, food, non-food, household or GMS &amp; fresh). The term is divided into three, namely, term I (1-10), term II (11-20) and term III (21-month end). The data is processed using software Rapidminer version 5. The data processing results show an association relationship in several terms, namely all divisions in term I have influence, term II has no influence, term III has influence. Food division in term I has an influence, term II has no influence, term III has an effect. The nonfood division in term I has no influence, term II has no influence, term III has no effect. The GMS division in term I has no influence, term II has no influence, term III has no effect. The fresh division in term I has influence, term II has influence, term III has no effect. By using the results of the analysis, floor display and promotion patterns can be adjusted according to the consumer's shopping patterns.</em><strong> </strong></p><p><strong>Abstrak dalam Bahasa Indonesia.</strong>Perkembangan perusahaan ritel di Indonesia yang cukup pesat menyebabkan perlunya pemanfaatan data sebagai dasar dalam pengambilan keputusan.  Sebagai salah satu toko ritel yang sedang berkembang, pola pemajangan floor diplay belum dikelola dengan baik dan belum dikaitkan dengan pola belanja konsumennya.  M<em>arket basket analysi</em><em>s merupakan salah satu teknik metoda</em> <em>data mining</em> untuk menentukan hubungan asosiasi pola belanja kosumen dalam suatu transaksi pembelian.  Penelitian ini bertujuan untuk mengetahui apakah terdapat pola asosiasi pada setiap termin pembelanjaan konsumen pada lima divisi produk supermarket (seluruh divisi, food, nonfood, household atau GMS &amp; fresh). Termin terbagi menjadi tiga yaitu, termin I (tanggal 1-10), termin II (tanggal 11-20) dan termin III (tanggal 21-akhir bulan).  Data diolah dengan menggunakan Software Rapidminer versi 5. Hasil pengolahan data menunjukkan adanya hubungan asosiasi pada beberapa termin yaitu Seluruh divisi dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III ada pengaruh. Divisi food dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III ada pengaruh.  Divisi nonfood dalam termin I tidak ada pengaruh, termin II tidak ada pengaruh, termin III tidak ada pengaruh. Divisi GMS dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III tidak ada pengaruh. Divisi fresh dalam termin I ada pengaruh, termin II ada pengaruh, termin III tidak ada pengaruh. Dengan menggunakan hasil analisis, pola pemajangan floor display dan promosi dapat diselaraskan sesuai dengan pola belanja konsumen tersebut.</p>

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