Data mining in the construction industry: Present status, opportunities, and future trends

Yan, Hang; Yang, Nian‐Yun; Peng, Yi; Ren, Yitian

doi:10.1016/j.autcon.2020.103331

Cited by 105 publications

(57 citation statements)

References 147 publications

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“…This interest reveals the growing scientific interest in this topic. This increase coincides with the overall interest in data mining applications for other industries [28,29]. All the selected studies were classified by year and the result can be seen in Figure 3.…”

Section: Scopussupporting

confidence: 57%

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A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

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Section: Scopussupporting

confidence: 57%

“…This interest reveals the growing scientific interest in this topic. This increase coincides with the overall interest in data mining applications for other industries [28,29]. A particular importance has to be given to the papers that garner the highest interest in the community, which is measured by the number of citations that a study has.…”

Section: Scopusmentioning

confidence: 74%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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“…Subsequently, this study follows the KDD concepts, consisting of data selection, preprocessing/cleaning, transformation, data mining, and interpretation [33], [34]. KDD is a noticeable method to find new relevant patterns from large quantities of potentially useful and meaningful [35]. Data mining is an unavoidable step of the KDD process in harvesting useful knowledge from the dataset.…”

Section: Methodsmentioning

confidence: 99%

Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise

Okfalisa

Angraini

Novi

et al. 2021

J. Teknol. dan Sist. Komput

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The rural development measurement is undoubtedly not easy due to its particular needs and conditions. This study classifies village performance from social, economic, and ecological indices. One thousand five hundred ninety-one villages from the Community and Village Empowerment Office at Riau Province, Indonesia, are grouped into five village maturation classes: very under-developed village, under-developed village, developing village, developed village, and independent village. To date, Density-based spatial clustering of applications with noise (DBSCAN) is utilized in mining 13 of the villages’ attributes. Python programming is applied to analyze and evaluate the DBSCAN activities. The study reveals the grouping’s silhouette coefficient values at 0.8231, thus indicating the well-being clustering performance. The epsilon and minimum points values are considered in DBSCAN evaluation with percentage splits simulation. This grouping can be used as guidelines for governments in analyzing the distribution of rural development subsidies more optimal.

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“…In the construction industry, DM from large amounts of data has become an essential method for information discovery. Energy, building occupant and occupancy actions, safety management, material efficiency, and textual information discovery are some of the most common DM application domains in the construction industry [42].…”

Section: ) Dm Is Used For the Construction Industrymentioning

confidence: 99%

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Khorshid

Abdulazeez

Sallow

2021

AJRCoS

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Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

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Data mining in the construction industry: Present status, opportunities, and future trends

Cited by 105 publications

References 147 publications

A Review of Data Mining Applications in Semiconductor Manufacturing

A Review of Data Mining Applications in Semiconductor Manufacturing

Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

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