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Journal articles on the topic 'Data mining applications'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.

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Kohli, Sankalp. "Data Mining and Its Applications in Higher Education." International Journal of Scientific Engineering and Research 11, no. 1 (2023): 51–60. https://doi.org/10.70729/se23125221124.

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Bhakare, Parimal, and Shaini Suraj. "Data Mining in Healthcare: Current Applications and Issues." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 1129–31. http://dx.doi.org/10.21275/sr221118072136.

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4

Rohini, M., and Marrynal S. Eastaff Mrs. "DATA MINING KNOWLEDGE DISCOVERYANDITS APPLICATIONS." Volume 7 Issue 11 7, no. 11 (2021): 1–3. https://doi.org/10.5281/zenodo.5675585.

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Data Mining and Knowledge Discovery is a tested companion investigated logical diary zeroing in on datamining. It is distributed by Springer Science + Business Media. Starting at 2012, the proofreader in-boss is Geoffrey 1, Webb. The act of data mining or information revelation, in spite of profoundly perplexing methods and applications,is dependent on an extremely straightforward idea. Gathering data from a breath of hotspots for the motivations behind analysis.Generally, data mining of hard drive recuperation is done to gather data which would then be able to be used toimprove a cycle or met

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Rahman, Nayem. "Data Mining Techniques and Applications." International Journal of Strategic Information Technology and Applications 9, no. 1 (2018): 78–97. http://dx.doi.org/10.4018/ijsita.2018010104.

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Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuo

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Barai, Sudhir Kumar. "DATA MINING APPLICATIONS IN TRANSPORTATION ENGINEERING." TRANSPORT 18, no. 5 (2003): 216–23. http://dx.doi.org/10.3846/16483840.2003.10414100.

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Data mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent time, data mining studies have been carried out in many engineering disciplines. In this paper the background of data mining and tools is introduced. Further applications of data mining to transportation engineering problems are reviewed. The application of data mining for typical example of ‘Vehicle Crash Study’ is demonstrated using commercially available data mining tool. The paper highlights the potential of data mining tool application in transportation engineering sector.

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Caby, Errol C. "Data Mining Using SAS Applications." Technometrics 46, no. 2 (2004): 260–61. http://dx.doi.org/10.1198/tech.2004.s805.

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Ghani, Rayid, and Carlos Soares. "Data mining for business applications." ACM SIGKDD Explorations Newsletter 8, no. 2 (2006): 79–81. http://dx.doi.org/10.1145/1233321.1233332.

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Apte, Chidanand, Bing Liu, Edwin P. D. Pednault, and Padhraic Smyth. "Business applications of data mining." Communications of the ACM 45, no. 8 (2002): 49–53. http://dx.doi.org/10.1145/545151.545178.

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Shortland, R., and R. Scarfe. "Data mining applications in BT." BT Technology Journal 25, no. 3-4 (2007): 272–77. http://dx.doi.org/10.1007/s10550-007-0084-7.

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Washio, Takashi. "Applications eligible for data mining." Advanced Engineering Informatics 21, no. 3 (2007): 241–42. http://dx.doi.org/10.1016/j.aei.2007.01.001.

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H, H. Kaleemullah, and A. S. Tincky Shalinee. "Data Mining Techniques and Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1139–42. http://dx.doi.org/10.22214/ijraset.2022.48009.

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Abstract: In recent days internet is considered as the main supply for searching the information and collecting data . The extraction of the data from the web offers several query results. Machine-controlled tools are needed through queries from the amount of pages by using the internet to spot the connected info. Data mining method is taken into account an efficient method of extracting the relevant information from databases. This method is employed for the pattern identification. Data mining could be a method that finds helpful patterns from great amount of knowledge. The paper discusses fe

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Mathew, Sibi. "Review on Practical Applications of Social Media Data Mining." International Journal of Scientific Engineering and Research 11, no. 1 (2023): 24–35. https://doi.org/10.70729/se23108203033.

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14

Li, Jing Zhu, Qian Li, Tai Yu Liu, and Wei Hong Niu. "Data Mining: Modeling, Algorithms, Applications and Systems." Advanced Materials Research 926-930 (May 2014): 2786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2786.

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Data mining is a multidisciplinary field of the 20th century gradually, this paper based on data mining modeling, algorithms, applications and software tools were reviewed, the definition of data mining, the scope and characteristics of the data sets and data mining various practical situations; summarizes the data mining in the practical application of the basic steps and processes; data mining tasks in a variety of applications and modeling issues were discussed; cited the current field of data mining is mainly popular algorithms, and algorithm design issues to consider briefly analyzed; ove

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Wang, Dong. "Educational data mining: Methods and applications." Applied and Computational Engineering 16, no. 1 (2023): 205–9. http://dx.doi.org/10.54254/2755-2721/16/20230892.

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Educational data mining is a rapidly growing field that applies various statistical and data mining techniques to analyze educational data. This paper provides a general review of the literature on educational data mining, focusing on the methods and applications. Methods used in education data mining include classification and clustering. A classification algorithm is a supervised learning technique that seeks to categorize a given set of data objects into specified categories, build a classification model using the input data that already exists, and then apply the model to categorize new da

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Khin, Sein Hlaing, and Myo Kay Khine Thaw Yin. "Applications, Techniques and Trends of Data Mining and Knowledge Discovery Database." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1604–6. https://doi.org/10.5281/zenodo.3591147.

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Data Mining and Knowledge Discovery is intended to be the best technical publication in the field providing a resource collecting relevant common methods and techniques. Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Besides, for the successful existence of any business, discovering underlying patterns in data is considered essential. This paper proposed about applications, techniques and trends of Data Mining and Knowledge Discovery Data

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17

Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for st

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Trivedi, Nripesh. "Data mining." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (2024): 1094. http://dx.doi.org/10.18535/ijsrm/v12i03.ec07.

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Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in ma

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19

Gupta, Priyanka, and Rajan Gupta. "Data Mining Framework for IoT Applications." International Journal of Computer Applications 174, no. 2 (2017): 4–7. http://dx.doi.org/10.5120/ijca2017915316.

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20

Bratko, Ivan, and Dorian �uc. "Qualitative Data Mining and Its Applications." Journal of Computing and Information Technology 11, no. 3 (2003): 145. http://dx.doi.org/10.2498/cit.2003.03.01.

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21

Tripathi, Ramesh Chandra. "An analysis of data mining applications." Asian Journal of Multidimensional Research 10, no. 11 (2021): 297–303. http://dx.doi.org/10.5958/2278-4853.2021.01096.x.

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22

Kusiak, A. "Data mining: manufacturing and service applications." International Journal of Production Research 44, no. 18-19 (2006): 4175–91. http://dx.doi.org/10.1080/00207540600632216.

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23

Chen, Lei-Da, Toru Sakaguchi, and Mark N. Frolick. "Data Mining Methods, Applications, and Tools." Information Systems Management 17, no. 1 (2000): 65–70. http://dx.doi.org/10.1201/1078/43190.17.1.20000101/31216.9.

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24

Benczúr, András A. "Affordable Supercomputing for Data Mining Applications." Procedia Computer Science 7 (2011): 136–38. http://dx.doi.org/10.1016/j.procs.2011.09.009.

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25

Shaheen, Muhammad, Muhammad Shahbaz, Zahoor ur Rehman, and Aziz Guergachi. "Data mining applications in hydrocarbon exploration." Artificial Intelligence Review 35, no. 1 (2010): 1–18. http://dx.doi.org/10.1007/s10462-010-9180-z.

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26

Han, Jiawei, Russ B. Altman, Vipin Kumar, Heikki Mannila, and Daryl Pregibon. "Emerging scientific applications in data mining." Communications of the ACM 45, no. 8 (2002): 54–58. http://dx.doi.org/10.1145/545151.545179.

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27

Mwitondi, Kassim S. "Statistical data mining using SAS applications." Journal of Applied Statistics 39, no. 10 (2012): 2302. http://dx.doi.org/10.1080/02664763.2012.682451.

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28

Cavdur, Fatih, and Soundar Kumara. "Network mining: Applications to business data." Information Systems Frontiers 16, no. 3 (2012): 473–90. http://dx.doi.org/10.1007/s10796-012-9355-z.

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29

Hassan Hashem, Soukaena, and Alaa H. AL-Hamami. "Privacy Preserving for Data Mining Applications." Engineering and Technology Journal 26, no. 5 (2008): 552–64. http://dx.doi.org/10.30684/etj.29.5.8.

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30

Pragati, Sharma, and Sanjiv Sharma Dr. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGIES AND MANAGEMENT RESEARCH 5, no. 2 :SE (2018): 166–77. https://doi.org/10.5281/zenodo.1202113.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining c

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31

Mathur, Ankita. "Real-Time Data Mining: Applications and Security Challenges." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2210–24. https://doi.org/10.22214/ijraset.2025.72681.

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Data mining is used to extract knowledge from huge amount of the data Today, Data mining helps different organizations focus on customer’s behavior patterns. The research scope of data mining extended in various fields. This paper, discusses the concept of data mining, important issues and applications. So there comes the need of powerful and most importantly automatic tools for uncovering valuable slots of organized information from tremendous amount of data. Considering social networking site or a search engine, they receive millions of queries every day. Firstly, the Database Management Sys

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32

Obenshain, Mary K. "Application of Data Mining Techniques to Healthcare Data." Infection Control & Hospital Epidemiology 25, no. 8 (2004): 690–95. http://dx.doi.org/10.1086/502460.

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AbstractA high-level introduction to data mining as it relates to surveillance of healthcare data is presented. Data mining is compared with traditional statistics, some advantages of automated data systems are identified, and some data mining strategies and algorithms are described. A concrete example illustrates steps involved in the data mining process, and three successful data mining applications in the healthcare arena are described.

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33

Tsuda, Koji. "Data Mining for Biologists." International Journal of Knowledge Discovery in Bioinformatics 3, no. 4 (2012): 1–14. http://dx.doi.org/10.4018/ijkdb.2012100101.

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In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support

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34

Dr., Shashidhar V., Kulkarni Sutej, B. Karia Suraj, Shetty Pranya, and M. H. Samyak. "A Review on Privacy Preserving Data Mining Techniques and Applications." International Journal of Multidisciplinary Research Transactions 6, no. 8 (2024): 1–25. https://doi.org/10.5281/zenodo.13371837.

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In the realm of data mining, techniques such as clustering, association rule mining, and classification serve as powerful tools to unveil concealed insights within datasets. However, the proliferation of sensitive personal information in data sources has sparked heightened privacy concerns. In response, a myriad of privacy-protection solutions have emerged. This paper aims to scrutinize the latest advancements in data mining privacy protection and furnish a comprehensive review of anonymization approaches. By delving into the intricacies of safeguarding personal data, the study contributes to

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35

AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.

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36

Maryoosh, Amal Abdulbaqi, and Enas Mohammed Hussein. "A Review: Data Mining Techniques and Its Applications." International Journal of Computer Science and Mobile Applications 10, no. 3 (2022): 1–14. http://dx.doi.org/10.47760/ijcsma.2022.v10i03.001.

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Data mining is a set of processes by which knowledge is extracted from huge amounts of data. Data mining is used to extract useful patterns and hidden information from this data. Machine learning techniques help in the comprehension of the hidden knowledge in the data. Data mining is considered an important field of research and is used in many different fields such as fraud detection, financial banking, education, healthcare, agriculture, industry, etc. In this paper, we will highlight some fundamentals of data mining and its applications. Also, we will conduct a comparative study among diffe

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37

Lin, En-Bing, and Yu-Ru Syau. "Comparisons between Rough Set Based and Computational Applications in Data Mining." International Journal of Machine Learning and Computing 4, no. 4 (2014): 328–32. http://dx.doi.org/10.7763/ijmlc.2014.v4.432.

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38

Verma, Jyoti, and Jaimin N. Undavia. "Data Mining in Indian Railways: A Survey to Analyze Applications of Data Mining." International Journal of Computer Applications 183, no. 2 (2021): 27–30. http://dx.doi.org/10.5120/ijca2021921296.

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39

Eiman, Alothali, Alashwal Hany, and Harous Saad. "Data stream mining techniques: a review." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 728–37. https://doi.org/10.12928/TELKOMNIKA.v17i2.11752.

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A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques fo

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40

Topalović, Amir, and Antonia Azzini. "Data Mining Applications in SMEs: An Italian Perspective." Business Systems Research Journal 11, no. 3 (2020): 127–46. http://dx.doi.org/10.2478/bsrj-2020-0031.

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AbstractBackgroundFrom the last decade, data mining techniques, employed in particular in customer relationship management, have assumed a key role in the profitability and operations of companies. To support small and medium companies (SMEs), several innovative and continuously improving tools have been developed that allow SMEs to utilize the internal and external data sources to increase their competitiveness.ObjectivesIn this paper, an analysis of the impact of digitalization, and in particular data mining techniques, in the context of SMEs development is presented.Methods/ApproachA review

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41

Harding, J. A., M. Shahbaz, Srinivas, and A. Kusiak. "Data Mining in Manufacturing: A Review." Journal of Manufacturing Science and Engineering 128, no. 4 (2005): 969–76. http://dx.doi.org/10.1115/1.2194554.

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The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing

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42

Fan, Xue Dong. "Clustering Analysis Based on Data Mining Applications." Applied Mechanics and Materials 303-306 (February 2013): 1026–29. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1026.

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Abstract. In this paper, a clustering algorithm based on data mining technology applications, the use of the extraction mode noise characteristics amount and pattern recognition algorithms, extraction and selection of the characteristic quantities of the three types of mode, carried out under the same working conditions data mining clustering analysis ultimately satisfying recognition.

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43

Wang, Pu. "Research on the Mechanism of Web Data Mining in Electronic Commerce Application." Applied Mechanics and Materials 687-691 (November 2014): 3003–6. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3003.

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At present, the growth of the Internet has brought us a vast amount of information that we can hardly deal with. To solve the flood of information, various data mining systems have been created to assist and augment this natural social process. Data minig recommender systems have been developed to automate the recommendation process. Data mining recommender systems can be found at many electronic commerce applications. In this paper, a recommendation mechanism of web data mining in electronic commerce application is given. Then, presents the workflow of the web data mining in electronic commce

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44

Deshpande, S. P., and V. M. Thakare. "Data Mining System and Applications: A Review." International Journal of Distributed and Parallel systems 1, no. 1 (2010): 32–44. http://dx.doi.org/10.5121/ijdps.2010.1103.

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45

Bhardwaj, Brijesh Kumar. "A Comparative Study on Data Mining Applications." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 902–4. http://dx.doi.org/10.26438/ijcse/v7i2.902904.

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46

Ara Shaikh, Asmat, Amala Nirmal Doss, Muthukumar Subramanian, Vipin Jain, Mohd Naved, and Md Khaja Mohiddin. "Major applications of data mining in medical." Materials Today: Proceedings 56 (2022): 2300–2304. http://dx.doi.org/10.1016/j.matpr.2021.11.642.

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Thillainayagam, Venkatesan. "Data Mining Techniques and Applications- A Review." i-manager's Journal on Software Engineering 6, no. 3 (2012): 44–48. http://dx.doi.org/10.26634/jse.6.3.1791.

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48

Shrestha, Sushil, and Manish Pokharel. "Data Mining Applications Used in Education Sector." Journal of Education and Research 10, no. 2 (2020): 27–51. http://dx.doi.org/10.3126/jer.v10i2.32721.

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The purpose of this work is to study the usage trends of Data Mining (DM) methods in education. It discusses different data mining techniques used for different types of educational data. The related papers were initially selected from the metadata containing words like Online Learning (OL) and Educational Data Mining (EDM). The papers were then filtered on the basis of DM algorithms, the purpose of study, and the types of data used. The findings suggested that EDM is the most commonly used technique for the prediction of students’ academic success, and the most used purpose is classification,

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Schietgat, Leander. "Graph-based data mining for biological applications." AI Communications 24, no. 1 (2011): 95–96. http://dx.doi.org/10.3233/aic-2010-0482.

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Niavand, Hossein, and Farzaneh Haghighat Nia. "Data Mining, Applications Tools in Insurance Strategies." Asian Journal of Management 11, no. 1 (2020): 38. http://dx.doi.org/10.5958/2321-5763.2020.00007.4.

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