research paper on data mining pdf download

Review Paper on Data Mining Techniques and Applications

International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Volume-7, Issue-2, March 2019

5 Pages Posted: 2 Mar 2020

GVMGC Sonipat

Date Written: MARCH 31, 2019

Data mining is the process of extracting hidden and useful patterns and information from data. Data mining is a new technology that helps businesses to predict future trends and behaviors, allowing them to make proactive, knowledge driven decisions. The aim of this paper is to show the process of data mining and how it can help decision makers to make better decisions. Practically, data mining is really useful for any organization which has huge amount of data. Data mining help regular databases to perform faster. They also help to increase the profit, because of the correct decisions made with the help of data mining. This paper shows the various steps performed during the process of data mining and how it can be used by various industries to get better answers from huge amount of data.

Keywords: Data Mining, Regression, Time Series, Prediction, Association

Suggested Citation: Suggested Citation

Anshu (Contact Author)

Do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, econometrics: econometric & statistical methods - special topics ejournal.

Subscribe to this fee journal for more curated articles on this topic

Web Technology eJournal

Decision-making & management science ejournal, data science, data analytics & informatics ejournal.

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

data mining techniques Recently Published Documents

Total documents.

• Latest Documents
• Most Cited Documents
• Contributed Authors
• Related Sources
• Related Keywords

Prediction of Skin Diseases Using Machine Learning

Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.

A Survey on Building Recommendation Systems Using Data Mining Techniques

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract

Diagnosis and detection of plant diseases using data mining techniques, location-based crime prediction using multiclass classification data mining techniques, an effective approach to test suite reduction and fault detection using data mining techniques.

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Dengue fever prediction modelling using data mining techniques, fake news detection using data mining techniques.

Nowadays, internet has been well known as an information source where the information might be real or fake. Fake news over the web exist since several years. The main challenge is to detect the truthfulness of the news. The motive behind writing and publishing the fake news is to mislead the people. It causes damage to an agency, entity or person. This paper aims to detect fake news using semantic search.

A Leading Indicator Approach with Data Mining Techniques in Analysing Bitcoin Market Value

Export citation format, share document.

Research on the Application of Data Mining Technology in the Quality Data of Technical College Students in Higher Vocational Colleges

• Conference paper
• First Online: 17 July 2024
• Cite this conference paper

• Pei Liu 17 , 18 &
• Xiaolan Guo 17 , 18  

Part of the book series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ((LNICST,volume 580))

Included in the following conference series:

• EAI International Conference, BigIoT-EDU

The Modern higher vocational education advocates the cultivation of college students’ professional quality, so educational organizations pay great attention to the development of college students’ professional quality and hope to analyze it from the perspective of data. In view of this requirement, this paper carries out research based on data mining technology, mainly introduces the basic concept of data mining technology, and then establishes the technology application system, and puts forward the application method of technology in the quality data of technical college students in higher vocational colleges. Through the research, combined with the method in the system, the role of data mining technology can be played to help analyze the data of college students’ professional literacy, understand the development of their literacy, so as to adjust the training direction in time, or make targeted decisions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Yadong, L.I.: Application of data mining technology in employment guidance of students in higher vocational colleges. Theory Pract. Innov. Entrep. (2019)

Google Scholar  

He, X.: RETRACTED: discussion on the application of computer technology in table tennis teaching in higher vocational colleges. In: Journal of Physics: Conference Series, vol. 1992(2):022049 (4pp) (2021)

Bao-Quan, M.I.: Application of data mining technology in teaching management in higher vocational colleges. Softw. Guid. (2018)

Liu, X.: The application of data mining technology in the teaching evaluation in colleges and universities. J. Comput. Theor. Nanosci. 14 (1), 7–12, 14 (2017)

Zhang, R., Zhang, Y.: Research and application of data mining technology in student information management (2017)

Ping, L.: A preliminary investigation and study on professional identity of rehabilitation technology vocational college students 2021 (1), 3

Sun, X.: Application of data mining technology in college mental health education (2022)

Maerker, M., Guran, S.H.: Application of data-mining technologies to predict Paleolithic site locations in the Zagros Mountains of Iran (2022)

Wang, G., Cao, W., Cheng, Y.: Application of data mining technology in college students’ employment analysis (2020)

Chen, Z., Liu, J.: Research on the application of data analysis in SPOC teaching in higher vocational colleges: a case study of psychological quality training course for college students. J. Comput. Theor. Nanosci. (2018)

Jiang, P., Tan, Y., Wen-Jun, W.U., et al.: Application of king standard theory in the practical teaching of basic nursing technology in higher vocational colleges. Smart Healthc. (2019)

Yang, Y., Pei, G.: Application of information technology in the course of food sculpture in higher vocational colleges. Mod. Food (2019)

Wei-Zhong, L.V.: Application of flipped classroom in the teaching of database application technology in higher vocational colleges. Comput. Knowl. Technol. (2018)

Shao, M.Y.: The application of information technology in ideological and political education in higher vocational colleges. J. Hubei Corresp. Univ. (2017)

Yang, Z., Department, H.Q.: Exploration and application of information technology in physical education teaching in higher vocational colleges. Educ. Teach. Forum (2018)

Wang, Y.T.: Application of modern educational technology in Chinese teaching in higher vocational colleges. J. Jiangxi Vocat. Tech. Coll. Electr. (2018)

Wang, G., Department, A.: Application of 3D printing technology in animation contrary teaching design for deaf students in higher vocational colleges. J. Chang. Univ. (2016)

Yuan, J., Branch, Z.: Application of information technology in the teaching of color class in higher vocational colleges. J. Zhenjiang Coll. (2016)

Xiao, J.Y.: Application of cloud computing technology in the teaching of computer courses in higher vocational colleges—taking the course of “Cloud Platform Construction and Application” as an example. J. Hebei Energy Inst. Vocat. Technol. (2019)

Zhao, C.M., University, B.: Discussion on the application of multimedia technology in music teaching in higher vocational colleges. J. Hubei Corresp. Univ. (2016)

Download references

Acknowledgements

specialized Project in the construction of Double high-level colleges. No.C103. Study on the Measures and Effects of Morality and Technical Education in Vocational Colleges.

Author information

Authors and affiliations.

Shandong Institute of Commerce and Technology, Jinan, 250103, Shandong, China

Pei Liu & Xiaolan Guo

School of Politics and Law, Baotou Teachers College, Inner Mongolia, 014030, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Pei Liu .

Editor information

Editors and affiliations.

Guangxi Science and Technology Normal University, Laibin, China

Yinjun Zhang

University of Swabi, Swabi, Pakistan

Rights and permissions

Reprints and permissions

Copyright information

© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper.

Liu, P., Guo, X. (2024). Research on the Application of Data Mining Technology in the Quality Data of Technical College Students in Higher Vocational Colleges. In: Zhang, Y., Shah, N. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 580. Springer, Cham. https://doi.org/10.1007/978-3-031-63130-6_27

Download citation

DOI : https://doi.org/10.1007/978-3-031-63130-6_27

Published : 17 July 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-63129-0

Online ISBN : 978-3-031-63130-6

eBook Packages : Computer Science Computer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Data Mining and Data Warehousing

Today in organizations, the developments in the transaction processing technology requires that, amount and rate of data capture should match the speed of processing of the data into information which can be utilized for decision making. A data warehouse is a subject- oriented, integrated, time-variant and non-volatile collection of data that is required for decision making process. Data mining involves the use of various data analysis tools to discover new facts, valid patterns and relationships in large data sets. The data warehouse supports on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. OLTP is customer-oriented and is used for transaction and query processing by clerks, clients and information technology professionals. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.

Related Papers

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

https://www.ijert.org/data-warehousing-and-olap-technologies-for-decision-making-process https://www.ijert.org/research/data-warehousing-and-olap-technologies-for-decision-making-process-IJERTV1IS6253.pdf Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies. Data warehousesprovide on-line analytical processing (OLAP) tools for theinteractive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. s. An OLAP system is market-oriented and is used for data analysis by knowledge workers, including managers, executives and analysts. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications.

ACM Sigmod record

Vijay Gupta

IJSRD - International Journal for Scientific Research and Development

— This paper describes the technology of data warehouse in decision making. Data warehousing and on-line analytical processing (OLAP) are prerequisite aspects of decision support, which has increasingly become a focus of the database industry. The construction of data warehouses involves data cleaning, data integration, data transformation and as important pre-processing step for data mining. The data warehouse supports on-line analytical processing (OLAP) which has the functional and performance requirements. Data warehousing have evolved as one of primary technologies that facilitate data storage, organization and, denoting retrieval. Different requirements on database technology compared to traditional on-line transaction processing applications.

Dr. Alexanda O . U . Kalu

shaik IMRAN pasha

Md. Nayem Khan

This paper provides an overview of Data warehousing and OLAP-OLTP technology exploring the significance of Industrial work like decision support. Data warehousing and OLAP (On-line Analytical Processing) tools are essential for decision-making and has the ability to focus on databases of industry. Today we can easily see the difference between the requirements of database technology compared to OLTP (On-line Transaction Processing) application. OLAP is market oriented where OLTP is customers transaction oriented. Moreover, we have some of the descriptions of back end tools for extracting and cleaning of data, multidimensional data models, front-end client tools, database query and analysis, metadata management of warehouse, industrial database management and some research about data warehousing and mining with OLAP and OLTP. In addition, here will be consideration of basic need of OLAP and OLTP in industry or business management and OLAP-OLTPs advantages and disadvantages.

International Journal IJRITCC

— Data mining is a process which is used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. It depends on constructive data collection and warehousing as well as computer processing. Data mining used to analyze patterns and relationships in data based on what users request. For example, data mining software can be used to create classes of information. When companies centralize their data into one database or program, it is known as data warehousing. Accompanied a data warehouse, an organization may spin off segments of the data for particular users and utilize. While, in other cases, analysts may begin with the type of data they want and create a data warehouse based on those specs. Regardless of how businesses and other entities systemize their data, they use it to support management's decision-making processes.

Australasian Journal of Information Systems

Sunil Samtani

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Dr. Moody A Amakobe

International Journal on Recent and Innovation Trends in Computing and Communication

Dhiraj Shirbhate

priya dharsnee

Encyclopedia of Decision Making and Decision Support Technologies

Qiyang Chen

Muhamed Smajli

American journal of Engineering Research (AJER)

Dagstuhl Reports

Gottfried Vossen

Sabrina Koffi

Decision Support Systems

MANISH BHARDWAJ

IJRIT Journal

Publisher ijmra.us UGC Approved

ACM SIGMOD Record

Il-Yeol Song

Arrahman Adnani

Filippo Tangorra , Ezio Lefons

Hugh J Watson

Manfred Jeusfeld

André Pires

… Joint Conference on INC, IMS and …

Communications of the ACM

Eduardo Juarez

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024