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Web Mining: A Survey of Current Research, Techniques, and Software

2008, International Journal of Information Technology & Decision Making

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applicat...

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anurag kumar

Web Mining is moving the World Wide Web towards a more useful environment in which users can quickly and easily find the information they need. Large amount of text documents, multimedia files and images are available in the web and it is still increasing. Data mining is the form of extracting data’s available in the internet. Web mining is a part of data mining. Web mining is used to discover and extract information from Web-related data sources such as Web documents, Web content, hyperlinks and server logs. The term Web mining has been used in three distinct ways. The first, called Web content mining is the process of information discovery from sources across the World Wide Web. The second, called Web structure mining is the process of analyzing the relationship between Web pages linked by information or direct link connection through the use of graph theory. The third, called Web usage mining is the process of extracting patterns and information from server logs to gain insight on user activity. In this paper, we are trying to give a brief idea regarding web mining concerned with its techniques, tools and applications.

Richard Segall

Venkata Ramana

brijesh singh

Bonfring International Journal

Web is a platforms for information exchange, as it is simple and easy to publish documents. Searching for information becomes a difficult and time-consuming process as the web grows. Web mining uses various data mining techniques to discover useful knowledge from usage log file from the web. The mining tools are used to scan the HTML documents, images, and text, the results is provided for the search engines.It can assist search engines in providing productive results of each search in order of their relevance. In this paper, we brief introduction to the concepts related to web mining and then an overview of different Web usage mining.

Dr. M.A.Dorairangaswamy

This study presents the role of Web mining an explosive growth of the World Wide Web; websites are providing an information and knowledge to the end users. This is the review paper which show deep and intense study of various technologies available for web mining and it is the application of data mining techniques to extract knowledge from web. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. Index Terms—web mining, web content mining, web usage mining, web structure mining.

aarti Pandey

Jawad Mughal

Research Publish Journals

Abstract: Web mining is a very hot research topic which combines two of the activated research areas: Data Mining and World Wide Web. The Web mining research relates to several research communities such as Database, Information Retrieval and Artificial Intelligence. Although there exists quite some confusion about the Web mining, the most recognized approach is to categorize Web mining into three areas: Web content mining, Web structure mining, and Web usage mining. Web content mining focuses on the discovery/retrieval of the useful information from the Web contents/data/documents, while the Web structure mining emphasizes to the discovery of how to model the underlying link structures of the Web. The distinction between these two categories isn't a very clear sometimes. Web usage mining is relative independent, but not isolated, category, which mainly describes the techniques that discover the user's usage pattern and try to predict the user's behaviors. This paper is a survey based on the recently published research papers. Besides providing an overall view of Web mining, this paper will focus on Web usage mining. Generally speaking, Web usage mining consists of three phases: Pre-processing, Pattern discovery and Pattern analysis. A detailed description will be given for each part of them, however, special attention will be paid to the user navigation patterns discovery and analysis. The user privacy is another important issue in this paper. An example of a prototypical Web usage mining system, WebSIFT, will be introduced to make it easier to understand the methodology of how to apply data mining techniques to large Web data repositories in order to extract usage patterns. Finally, along with some other interested research issues, a brief overview of the current research work in the area of Web usage mining is included. Title: WEB MINING AN APPLICATION OF DATA MINING Author: Sumit Dalal, Sumit Kumar, Vivek Dixit International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online), ISSN 2348-1196 (print) Research Publish Journals

maithreyan surya

The World Wide Web is a popular and interactive medium to disseminate information today. It is a system of interlinked hypertext documents accessed via the Internet. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks. With the recent explosive growth of the amount of content on the Internet, it has become increasingly difficult for users to find and utilize information and for content providers to classify and catalog documents on the World Wide Web. Traditional web search engines often return hundreds or thousands of results for a search, which is time consuming for users to browse. On-line libraries, search engines, and other large document repositories (e.g. customer support databases, product specification databases, press release archives, news story archives, etc.) are growing so rapidly that it is difficult and costly to categorize every document manually. To deal with these problems web mining is used. Web mining is the use of data mining techniques to automatically discover and extract information from the web documents and services. This paper presents an overview of web mining, its methodologies, algorithms and applications.

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Computer Science > Machine Learning

Title: web mining research: a survey.

Abstract: With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

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DOI: 10.14569/IJACSA.2021.0120886 PDF

A Systematic Review Web Content Mining Tools and its Applications

Author 1: Manjunath Pujar Author 2: Monica R Mundada

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 8, 2021.

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Abstract: In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

Manjunath Pujar and Monica R Mundada, “A Systematic Review Web Content Mining Tools and its Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 12(8), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120886

@article{Pujar2021, title = {A Systematic Review Web Content Mining Tools and its Applications}, journal = {International Journal of Advanced Computer Science and Applications}, doi = {10.14569/IJACSA.2021.0120886}, url = {http://dx.doi.org/10.14569/IJACSA.2021.0120886}, year = {2021}, publisher = {The Science and Information Organization}, volume = {12}, number = {8}, author = {Manjunath Pujar and Monica R Mundada} }

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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A comprehensive survey of data mining

• Original Research
• Published: 06 February 2020
• Volume 12 , pages 1243–1257, ( 2020 )

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• Manoj Kumar Gupta   ORCID: orcid.org/0000-0002-4481-8432 1 &
• Pravin Chandra 1  

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Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this paper. The challenges and issues in area of data mining research are also presented in this paper.

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• Artificial Intelligence

Fayadd U, Piatesky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AAAI Press/The MIT Press, Massachusetts Institute of Technology. ISBN 0–262 56097–6 Fayap

Fayadd U, Piatesky-Shapiro G, Smyth P (1996) Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the 2nd ACM international conference on knowledge discovery and data mining (KDD), Portland, pp 82–88

Heikki M (1996) Data mining: machine learning, statistics, and databases. In: SSDBM ’96: proceedings of the eighth international conference on scientific and statistical database management, June 1996, pp 2–9

Arora RK, Gupta MK (2017) e-Governance using data warehousing and data mining. Int J Comput Appl 169(8):28–31

Google Scholar  

Morik K, Bhaduri K, Kargupta H (2011) Introduction to data mining for sustainability. Data Min Knowl Discov 24(2):311–324

Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier, Netherlands

MATH   Google Scholar  

Friedman JH (1997) Data mining and statistics: What is the connection? in: Keynote Speech of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, TX, 1997

Turban E, Aronson JE, Liang TP, Sharda R (2007) Decision support and business intelligence systems. 8 th edn, Pearson Education, UK

Gheware SD, Kejkar AS, Tondare SM (2014) Data mining: tasks, tools, techniques and applications. Int J Adv Res Comput Commun Eng 3(10):8095–8098

Kiranmai B, Damodaram A (2014) A review on evaluation measures for data mining tasks. Int J Eng Comput Sci 3(7):7217–7220

Sharma M (2014) Data mining: a literature survey. Int J Emerg Res Manag Technol 3(2):1–4

Venkatadri M, Reddy LC (2011) A review on data mining from past to the future. Int J Comput Appl 15(7):19–22

Chen M, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883

Gupta MK, Chandra P (2019) A comparative study of clustering algorithms. In: Proceedings of the 13th INDIACom-2019; IEEE Conference ID: 461816; 6th International Conference on “Computing for Sustainable Global Development”

Ponniah P (2001) Data warehousing fundamentals. Wiley, USA

Chandra P, Gupta MK (2018) Comprehensive survey on data warehousing research. Int J Inform Technol 10(2):217–224

Weiss SH, Indurkhya N (1998) Predictive data mining: a practical guide. Morgan Kaufmann Publishers, San Francisco

Fu Y (1997) Data mining: tasks, techniques, and applications. IEEE Potentials 16(4):18–20

Abuaiadah D (2015) Using bisect k-means clustering technique in the analysis of arabic documents. ACM Trans Asian Low-Resour Lang Inf Process 15(3):1–17

Algergawy A, Mesiti M, Nayak R, Saake G (2011) XML data clustering: an overview. ACM Comput Surv 43(4):1–25

Angiulli F, Fassetti F (2013) Exploiting domain knowledge to detect outliers. Data Min Knowl Discov 28(2):519–568

MathSciNet   MATH   Google Scholar  

Angiulli F, Fassetti F (2016) Toward generalizing the unification with statistical outliers: the gradient outlier factor measure. ACM Trans Knowl Discov Data 10(3):1–26

Bhatnagar V, Ahuja S, Kaur S (2015) Discriminant analysis-based cluster ensemble. Int J Data Min Modell Manag 7(2):83–107

Bouguessa M (2013) Clustering categorical data in projected spaces. Data Min Knowl Discov 29(1):3–38

MathSciNet   Google Scholar  

Campello RJGB, Moulavi D, Zimek A, Sander J (2015) Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans Knowl Discov Data 10(1):1–51

Carpineto C, Osinski S, Romano G, Weiss D (2009) A survey of web clustering engines. ACM Comput. Surv. 41(3):1–38

Ceglar A, Roddick JF (2006) Association mining. ACM Comput Surv 38(2):1–42

Chen YL, Weng CH (2009) Mining fuzzy association rules from questionnaire data. Knowl Based Syst 22(1):46–56

Fan Chin-Yuan, Fan Pei-Shu, Chan Te-Yi, Chang Shu-Hao (2012) Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Syst Appl 39:8844–8851

Das R, Kalita J, Bhattacharya (2011) A pattern matching approach for clustering gene expression data. Int J Data Min Model Manag 3(2):130–149

Dincer E (2006) The k-means algorithm in data mining and an application in medicine. Kocaeli Univesity, Kocaeli

Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv 38(3):1–32

Gupta MK, Chandra P (2019) P-k-means: k-means using partition based cluster initialization method. In: Proceedings of the international conference on advancements in computing and management (ICACM 2019), Elsevier SSRN, pp 567–573

Gupta MK, Chandra P (2019) An empirical evaluation of k-means clustering algorithm using different distance/similarity metrics. In: Proceedings of the international conference on emerging trends in information technology (ICETIT-2019), emerging trends in information technology, LNEE 605 pp 884–892 DOI: https://doi.org/10.1007/978-3-030-30577-2_79

Hea Z, Xua X, Huangb JZ, Denga S (2004) Mining class outliers: concepts, algorithms and applications in CRM. Expert Syst Appl 27(4):681e97

Hung LN, Thu TNT, Nguyen GC (2015) An efficient algorithm in mining frequent itemsets with weights over data stream using tree data structure. IJ Intell Syst Appl 12:23–31

Hung LN, Thu TNT (2016) Mining frequent itemsets with weights over data stream using inverted matrix. IJ Inf Technol Comput Sci 10:63–71

Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput. Surv 31(3):1–60

Jin H, Wang S, Zhou Q, Li Y (2014) An improved method for density-based clustering. Int J Data Min Model Manag 6(4):347–368

Khandare A, Alvi AS (2017) Performance analysis of improved clustering algorithm on real and synthetic data. IJ Comput Netw Inf Secur 10:57–65

Koh YS, Ravana SD (2016) Unsupervised rare pattern mining: a survey. ACM Trans Knowl Discov Data 10(4):1–29

Kosina P, Gama J (2015) Very fast decision rules for classification in data streams. Data Min Knowl Discov 29(1):168–202

Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

Kumar D, Bezdek JC, Rajasegarar S, Palaniswami M, Leckie C, Chan J, Gubbi J (2016) Adaptive cluster tendency visualization and anomaly detection for streaming data. ACM Trans Knowl Discov Data 11(2):1–24

Lee G, Yun U (2017) A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Gener Comput Syst 68:89–110

Li G, Zaki MJ (2015) Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Discov 30(1):181–225. https://doi.org/10.1007/s10618-015-0409-y

Article   MathSciNet   MATH   Google Scholar  

Liao TW, Triantaphyllou E (2007) Recent advances in data mining of enterprise data: algorithms and applications. World Scientific Publishing, Singapore, pp 111–145

Mabroukeh NR, Ezeife CI (2010) A taxonomy of sequential pattern mining algorithms. ACM Comput Surv 43:1

Mampaey M, Vreeken J (2011) Summarizing categorical data by clustering attributes. Data Min Knowl Discov 26(1):130–173

Menardi G, Torelli N (2012) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28(1):4–28. https://doi.org/10.1007/s10618-012-0295-5

Mukhopadhyay A, Maulik U, Bandyopadhyay S (2015) A survey of multiobjective evolutionary clustering. ACM Comput Surv 47(4):1–46

Pei Y, Fern XZ, Tjahja TV, Rosales R (2016) ‘Comparing clustering with pairwise and relative constraints: a unified framework. ACM Trans Knowl Discov Data 11:2

Rafalak M, Deja M, Wierzbicki A, Nielek R, Kakol M (2016) Web content classification using distributions of subjective quality evaluations. ACM Trans Web 10:4

Reddy D, Jana PK (2014) A new clustering algorithm based on Voronoi diagram. Int J Data Min Model Manag 6(1):49–64

Rustogi S, Sharma M, Morwal S (2017) Improved Parallel Apriori Algorithm for Multi-cores. IJ Inf Technol Comput Sci 4:18–23

Shah-Hosseini H (2013) Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm. Int J Data Min Model Manag 5(4):301–317

Silva JA, Faria ER, Barros RC, Hruschka ER, de Carvalho ACPLF, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46(1):1–31

Silva A, Antunes C (2014) Multi-relational pattern mining over data streams. Data Min Knowl Discov 29(6):1783–1814. https://doi.org/10.1007/s10618-014-0394-6

Sim K, Gopalkrishnan V, Zimek A, Cong G (2012) A survey on enhanced subspace clustering. Data Min Knowl Discov 26(2):332–397

Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Hum Behav 68:244–253

Craw Susan, Wiratunga Nirmalie, Rowe Ray C (2006) Learning adaptation knowledge to improve case-based reasoning. Artif Intell 170:1175–1192

Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36(4):8616–8630

Tew C, Giraud-Carrier C, Tanner K, Burton S (2013) Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min Knowl Discov 28(4):1004–1045

Wang L, Dong M (2015) Exemplar-based low-rank matrix decomposition for data clustering. Data Min Knowl Discov 29:324–357

Wang F, Sun J (2014) Survey on distance metric learning and dimensionality reduction in data mining. Data Min Knowl Discov 29:534–564

Wang B, Rahal I, Dong A (2011) Parallel hierarchical clustering using weighted confidence affinity. Int J Data Min Model Manag 3(2):110–129

Zacharis NZ (2018) Classification and regression trees (CART) for predictive modeling in blended learning. IJ Intell Syst Appl 3:1–9

Zhang W, Li R, Feng D, Chernikov A, Chrisochoides N, Osgood C, Ji S (2015) Evolutionary soft co-clustering: formulations, algorithms, and applications. Data Min Knowl Discov 29:765–791

Han J, Fu Y (1996) Exploration of the power of attribute-oriented induction in data mining. Adv Knowl Discov Data Min. AAAI/MIT Press, pp 399-421

Gupta A, Mumick IS (1995) Maintenance of materialized views: problems, techniques, and applications. IEEE Data Eng Bull 18(2):3

Sawant V, Shah K (2013) A review of distributed data mining using agents. Int J Adv Technol Eng Res 3(5):27–33

Gupta MK, Chandra P (2019) An efficient approach for selection of initial cluster centroids for k-means clustering algorithm. In: Proceedings international conference on recent developments in science engineering and technology (REDSET-2019), November 15–16 2019

Gupta MK, Chandra P (2019) MP-K-means: modified partition based cluster initialization method for k-means algorithm. Int J Recent Technol Eng 8(4):1140–1148

Gupta MK, Chandra P (2019) HYBCIM: hypercube based cluster initialization method for k-means. IJ Innov Technol Explor Eng 8(10):3584–3587. https://doi.org/10.35940/ijitee.j9774.0881019

Article   Google Scholar  

Enke David, Thawornwong Suraphan (2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29:927–940

Mezyk Edward, Unold Olgierd (2011) Machine learning approach to model sport training. Comput Hum Behav 27:1499–1506

Esling P, Agon C (2012) Time-series data mining. ACM Comput Surv 45(1):1–34

Hüllermeier Eyke (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156:387–406

Hullermeier Eyke (2011) Fuzzy sets in machine learning and data mining. Appl Soft Comput 11:1493–1505

Gengshen Du, Ruhe Guenther (2014) Two machine-learning techniques for mining solutions of the ReleasePlanner™ decision support system. Inf Sci 259:474–489

Smith Kate A, Gupta Jatinder ND (2000) Neural networks in business: techniques and applications for the operations researcher. Comput Oper Res 27:1023–1044

Huang Mu-Jung, Tsou Yee-Lin, Lee Show-Chin (2006) Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowl Based Syst 19:396–403

Padhraic S (2000) Data mining: analysis on grand scale. Stat Method Med Res 9(4):309–327. https://doi.org/10.1191/096228000701555181

Article   MATH   Google Scholar  

Saeed S, Ali M (2012) Privacy-preserving back-propagation and extreme learning machine algorithms. Data Knowl Eng 79–80:40–61

Singh Y, Bhatia PK, Sangwan OP (2007) A review of studies on machine learning techniques. Int J Comput Sci Secur 1(1):70–84

Yahia ME, El-taher ME (2010) A new approach for evaluation of data mining techniques. Int J Comput Sci Issues 7(5):181–186

Jackson J (2002) Data mining: a conceptual overview. Commun Assoc Inf Syst 8:267–296

Heckerman D (1998) A tutorial on learning with Bayesian networks. Learning in graphical models. Springer, Netherlands, pp 301–354

Politano PM, Walton RO (2017) Statistics & research methodol. Lulu. com

Wetherill GB (1987) Regression analysis with application. Chapman & Hall Ltd, UK

Anderberg MR (2014) Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, vol 19. Academic Press, USA

Mihoci A (2017) Modelling limit order book volume covariance structures. In: Hokimoto T (ed) Advances in statistical methodologies and their application to real problems. IntechOpen, Croatia. https://doi.org/10.5772/66152

Chapter   Google Scholar  

Thompson B (2004) Exploratory and confirmatory factor analysis: understanding concepts and applications. American Psychological Association, Washington, DC (ISBN:1-59147-093-5)

Kuzey C, Uyar A, Delen (2014) The impact of multinationality on firm value: a comparative analysis of machine learning techniques. Decis Support Syst 59:127–142

Chan Philip K, Salvatore JS (1997) On the accuracy of meta-learning for scalable data mining. J Intell Inf Syst 8:5–28

Tsai Chih-Fong, Hsu Yu-Feng, Lin Chia-Ying, Lin Wei-Yang (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36:11994–12000

Liao SH, Chu PH, Hsiao PY (2012) Data mining techniques and applications—a decade review from 2000 to 2011. Expert Syst Appl 39:11303–11311

Kanevski M, Parkin R, Pozdnukhov A, Timonin V, Maignan M, Demyanov V, Canu S (2004) Environmental data mining and modelling based on machine learning algorithms and geostatistics. Environ Model Softw 19:845–855

Jain N, Srivastava V (2013) Data mining techniques: a survey paper. Int J Res Eng Technol 2(11):116–119

Baker RSJ (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds) International encyclopedia of education, 3rd edn. Elsevier, Oxford, UK

Lew A, Mauch H (2006) Introduction to data mining and its applications. Springer, Berlin

Mukherjee S, Shaw R, Haldar N, Changdar S (2015) A survey of data mining applications and techniques. Int J Comput Sci Inf Technol 6(5):4663–4666

Data mining examples: most common applications of data mining (2019). https://www.softwaretestinghelp.com/data-mining-examples/ . Accessed 27 Dec 2019

Devi SVSG (2013) Applications and trends in data mining. Orient J Comput Sci Technol 6(4):413–419

Data mining—applications & trends. https://www.tutorialspoint.com/data_mining/dm_applications_trends.htm

Keleş MK (2017) An overview: the impact of data mining applications on various sectors. Tech J 11(3):128–132

Top 14 useful applications for data mining. https://bigdata-madesimple.com/14-useful-applications-of-data-mining/ . Accessed 20 Aug 2014

Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Making 5(4):597–604

Padhy N, Mishra P, Panigrahi R (2012) A survey of data mining applications and future scope. Int J Comput Sci Eng Inf Technol 2(3):43–58

Gibert K, Sanchez-Marre M, Codina V (2010) Choosing the right data mining technique: classification of methods and intelligent recommendation. In: International Congress on Environment Modelling and Software Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa, Canada

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Gupta, M.K., Chandra, P. A comprehensive survey of data mining. Int. j. inf. tecnol. 12 , 1243–1257 (2020). https://doi.org/10.1007/s41870-020-00427-7

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Received : 29 June 2019

Accepted : 20 January 2020

Published : 06 February 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s41870-020-00427-7

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