Data Mining Course Research Articles

The application of naive bayes method for final project topic selection within the project-based learning framework in the data mining course

<p>This study aims to explore the potential application of the Naive Bayes Method in predicting the determination of final project topics for students within the context of project-based learning (PBL) in the Data Mining course. Adopting an observational quantitative approach, the research involved 34 students from the Educational Technology Informatics Study Program at Padang State University between 2013-2015 as research subjects. The sampling technique employed was stratified random sampling, aligning with the research findings. The study's results indicate that the Naive Bayes Method is proficient in providing accurate predictions concerning the determination of final project topics, with significant accuracy, precision, and recall values. Moreover, the integration with project-based learning (PjBL) approach effectively enhances the authenticity and relevance of the learning experience for students, further substantiating the effectiveness of Project-Based Learning (PjBL).</p>

Research on Curriculum Design of Data Mining in Different Majors

With the increasing attention paid to the application value of big data, data mining has become an important major course not only in big data, computer and information technology, but also in most majors in colleges and universities. This course involves the explanation of classic algorithms of data mining as well as the application of classical algorithms of data mining, so it usually has both theoretical and practical hours in the composition of teaching form. Because of the differences in the knowledge structure of students in different majors, there should be differences in the theoretical teaching and practical teaching design of data mining courses among different majors. The teaching content should be selected according to the characteristics and needs of students in different majors, and should also consider the development and trends of big data technology. In addition, in order to improve the teaching quality and effectiveness of data mining courses, it is also necessary to use modern teaching methods and technologies to support teaching, such as online teaching resources, flipped classroom teaching, etc. In short, it is necessary to carry out targeted teaching design for data mining courses in different majors to match the basic situation of students in different majors, ensure better teaching effect and play a better role in different fields of specialization.

Exploring the Applications of EduCoder Platform in Blended Teaching for Computer Major

With the increasing demands of computer science-related human resources, universities have gradually adjusted the curricular scheme of undergraduate students, incorporating more computer-oriented courses, such as data mining, Python programming, and database application. Conventional off-line teaching is limited to imparting theoretical knowledge to students, failing to enhance their practical abilities, which are crucial in the realm of computer-oriented courses. Therefore, based on our practical teaching experience about data mining course, we analyze the benefits and requirements of blended teaching, then leverage the distinctive characteristics and flexible features of EduCoder platform, and propose its various application approaches for computer-related majors, encompassing three aspects, i.e., daily class management, online experiment designing, and statistical analysis of studying performance. Benefiting from these application approaches, it could reduce repeated labors, alleviate teachers’ workload, enhance teaching efficiency, and improve the overall quality of practical teaching.

Research and Practice of Ideological and Political Education in the Course of Data Mining and Analysis

The cultivation of moral character and the education of students are the fundamental tasks of higher education. In order to implement this fundamental task, this paper focuses on the course objective of “combining knowledge imparting with value guidance”, and based on the characteristics of the course, explores and investigates the ways and methods of integrating ideological and political education into the teaching of professional courses in the construction and implementation of the “Data Mining and Analysis” course. This paper mainly explores the integration of ideological and political elements into the teaching of the course from four aspects: course objectives, teaching design, teaching process, and teaching evaluation. By integrating ideological and political elements into various aspects of teaching in a planned manner, moral education can penetrate and achieve the goal of “ideological and political education entering the hearts of students and influencing them silently”. This research has a certain universality and can provide useful reference for the practical teaching of ideological and political education in relevant colleges and universities.

A Review on Data Mining Technology

Presently days the method involved with removing significant principles from large and complex data are called data mining. Data mining has a rising fame in each field today. Data units are laid out in client situated enterprises, for example, showcasing, money and telecom to deal with the client beat and obtaining, specifically. Among the data mining techniques, order calculations are utilised in examinations led for client securing to anticipate the likely clients of the organization being referred to in the connected business. The idea of Data Mining has arisen to meet the prerequisite of speedy and exact data support for dynamic cycle. Data Mining is investigation of data to distinguish connection between various data components or substances. The course of data mining can likewise include connection or relationship between at least two data components, substances or occasions. They permit associations to make proactive, information-driven choices and answer questions that were already excessively tedious to determine. In this exploration we have zeroed in on the correlation of different Data Mining apparatuses which are useful and set apart as the significant field of data mining Technologies. As we know that numerous public and Multinational organisations and little or enormous associations are worked in better places of the various nations. Every activity might produce large data or unstructured. This sort of huge data is accessible as byte which has definitely changed in different regions. To examine, oversee and pursue a choice of such sort of colossal measure of data we really want strategies called the data mining which will changing in many fields. Bank advertising data set in UCI Machine Learning Data Set was utilised by making models with similar grouping calculations in various data mining programs. Exactness, accuracy and measure measures were utilised to test exhibitions of the characterisation models. While making the grouping models, the test and preparing data sets were arbitrarily separated by the holdout technique to assess the exhibition of the data set.

Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study.

BackgroundMedical postgraduates’ demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important.ObjectiveThis study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school.MethodsWe developed a medical data mining course called “Practical Techniques of Medical Data Mining” for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms—Rain Classroom, Tencent Meeting, and WeChat—were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience.ResultsIn total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students’ characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated “very positive” or “positive”), and MedHub received the best feedback, both in function (80/83, 96% chose “satisfied”) and teaching effect (80/83, 96% chose “satisfied”). The case study showed that our course was able to fill the gap between student expectations and learning outcomes.ConclusionsWe developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

Data mining techniques: Which one is your favorite?

Based on a statistical analysis, undergraduate business students are shown to prefer classification tree over six other standard data mining techniques. Data were collected over a 4-year period from students taking a data mining course offered at a business university in the US. The principal reason given by students for this preference is that classification tree is a highly visual technique—a quality that makes its results relatively easy to comprehend. Educators can draw on this information in their classroom practice with both business students and students in other majors taking data mining courses. Researchers can benefit from it in relation to effectively presenting data mining results to multiple audiences.

Integration of data mining course in computer science curriculum

Data mining combines tools from statistics, neural networks, and machine learning with database management to analyze large data sets. It is a well-researched area of computer science with high dem...

Automatic text summarization within big data frameworks

Data increasing at unabated rates will prove to be challenging for individuals trying to effectively leverage it. This paper describes an approach to automatically summarize text documents by utili...

Concealment Conserving the Data Mining of Groups & Individual

We present an overview of privacy preserving data mining, one of the most popular directions in the data mining research community. In the first part of the chapter, we presented approaches that have been proposed for the protection of either the sensitive data itself in the course of data mining or the sensitive data mining results, in the context of traditional (relational) datasets. Following that, in the second part of the chapter, we focused our attention on one of the most recent as well as prominent directions in privacy preserving data mining: the mining of user mobility data. Although still in its infancy, privacy preserving data mining of mobility data has attracted a lot of research attention and already counts a number of methodologies both with respect to sensitive data protection and to sensitive knowledge hiding. Finally, in the end of the chapter, we provided some roadmap along the field of privacy preserving mobility data mining as well as the area of privacy preserving data mining at large.

Teaching Content Design and Organization of Data Mining Course for Statistics Postgraduates

The data mining course is very important course for statistics postgraduates. In this paper, firstly, the theory teaching content of data mining course is introduced. It contains ten parts, that are data mining concept, the data warehouse, data pre-processing, association rules, clustering algorithm, the decision tree algorithm, the neural network, intelligent optimization algorithm, web mining, recent advances in data mining. Secondly, the experimental teaching content is introduced. It contains six experiments. Finally a teaching case is introduced. The case is about the Jingdezhen Changjiang water quality prediction which uses the neural network and intelligent optimization algorithm which are described in the theory and experiment part.

A Comparative Analysis on the Applicability of Various Mutation Types for Achieving Privacy in medical Data Mining

Achieving privacy in data mining is at present an energetic area of research. During the entire course of data mining a variety of sensitive data gets exposed to several parties. This disclosure of insightful data violates individual privacy. In order to achieve privacy many privacy preserving techniques have been proposed recently and one such technique to preserve privacy is to use various operators in genetic algorithm (GA). In this paper we compare the various mutation techniques in genetic algorithm and their appropriateness to achieve data privacy. From our experimental results it is evident that the outcome of the mutation process depends on various parameters such as type of data and the binary pattern used for replacement.

Introductory course in data mining

A recent growth in the generation of data and information requires efficient and sophisticated algorithms and methodologies for intelligent and automated analysis of databases. These algorithms and...

LISp-Miner Control Language description of scripting language implementation

This paper introduces the LISp-Miner Control Language – a scripting language for the LISp-Miner system, an academic system for knowledge discovery in databases. The main purpose of this language is to provide programmable means to all the features of the LISp-Miner system and mainly to automate the main phases of data mining – from data introduction and preprocessing, formulation of analytical tasks, to discovery of the most interesting patterns. In this sense, the language is a necessary prerequisite for the EverMiner project of data mining automation. Language will serve other purposes too – for an automated verification of the LISp-Miner system functionality before a new version is released and as an educational tool in advanced data mining courses.

Development and assessment of ecological models in the context of the European Water Framework Directive: Key issues for trainers in data-driven modeling approaches

Teaching students to develop data-driven models is a challenging task as a good balance has to be found between the theoretical background of the models, the ecological relevance of the knowledge rules inferred and their socio-economic applicability. In this context it is unclear which aspects of the modeling process are easily understood by students, and in particular, how theoretical issues interfere with practical boundary conditions and socio-economic relevance (ecosystem protection, water management, policy development, ecological engineering). In order to fill this knowledge gap, students developed static data-driven models and tutors assessed students' performances. Criteria such as the theoretical, ecological and socio-economic relevance of the derived knowledge rules were used to select the most optimal models.We noticed an inverse relationship between the complexity of the subtasks and the number of students that succeeded. Students evaluated their models with respect to the theoretical reliability, but were not likely to consider the other two criteria. Half of the students succeeded in assessing the models based on their ecological relevance and only 17% of the students checked the socio-economic relevance of the knowledge rules. Four groups out of seven assessed their models merely based on the predictive power of the models. Only one group integrated the theoretical, ecological and socio-economic relevance to assess the models.The key findings of our research can be used to optimize the efficiency of data mining courses. We reveal which aspects of the modeling process students seem to overemphasize and give recommendations about the topics trainers should emphasize in the future to ensure that students develop advanced skills. Based on our results the theory–practice dichotomy in higher education can be further reduced. Our learning-by-doing approach showed students how to solve common problems in ecological data sets (e.g. missing data, outliers, collinearity, non-normal distribution, parameterization, uncertainty, etc.), which are often only briefly discussed in basic statistical courses.

Improving non-small cell lung cancer classification in data mining courses

Data mining courses have become popular electives in many graduate and undergraduate computer science curriculums. In these courses we usually give a comprehensive coverage of data mining concepts,...

Lessons learned in the design of an undergraduate data mining course

The ability to analyze and construct knowledge from data is a fundamental talent that is in high demand. Developing this ability requires the understanding of how to solve problems that are data or...

Designing an undergraduate data mining course by matching teaching strategies with student learning styles

Regularly integrating emerging technology to keep the curriculum up to date is generally expected in the rapidly evolving discipline of Computer Science. Due to the explosion of data and informatio...

Would Cloud Computing Revolutionize Teaching Business Intelligence Courses?

Introduction For the last several years, Masters of Business Administration (MBA) programs have been under mounting pressure to provide to prepare their students to become the next generation of Business Intelligence (BI) workers. In a recent survey of professors from more than 80 universities around the world (Betts & Kanaracus, 2010), Barbara Wixom discovered that 43% of the respondents reported that they can't adequately teach BI courses because they lack access to the needed software, hardware and real-world problems. She also indicates that It's clear from the survey that instructors want to change the way students learn about intelligence. Professors want to provide large data sets, contemporary software tools, and real-world content within their classrooms. But, factors like high technology costs, complex maintenance requirements, and steep learning curves present insurmountable obstacles. With the present economic crisis and its repercussions on the academic environments, one of the major hurdles in offering Business Intelligence is the complex and costly computing infrastructure necessary for the software used in such courses. On the other hand, unless Business Intelligence becomes a mainstream field at schools, the shortage of intelligence related professions will continue to rise. The cloud computing, also known as software-as-a-service, delivery model has the potential to become a major force in the future of offering BI courses. After a decade of growth and adoption, the commercial cloud computing offerings came into existence and products like Amazon EC2, Microsoft Azure and Salesforce.com helped to popularize it. The concept of on-demand BI promises to make it affordable for schools to deliver quality BI instruction to MBA students. Computer Science programs were among the first to develop experimental undergraduate data mining as reported by Lu and Bettine (2003). Banks, Dong, Liu and Mandvikar (2004) report on teaching data mining as exciting addition to the curriculum at the senior/graduate level that provide the opportunity to apply computer related education to various domains and applications. Paper by Roiger (2005) presents a very good tutorial on data mining that can be used by educators as an introduction to intelligence. The typical structure of a data warehousing and data mining course in the Computer Science curriculum is well described by Fang and Tuladhar (2006) and Musicant (2006). Information systems programs at schools typically offer BI as elective (Watson, 2006). A good example of an applied BI course that is designed to appeal to the graduate students of the CIS programs as well as MBA students selecting the CIS concentration is presented by Mrdalj (2007). Specifically, the structure and components of this course focus on business aspects of data mining and data warehousing by having students learn how to answer today's BI questions. In one of the rare papers related to MBA programs, Mrdalj and Diallo (2010) present the structure and components of a BI module that is designed to appeal to students of the finance program as well as to MBA students selecting the finance concentration. In the next section of this paper we stage our proposed solution with a brief presentation of the typical computing infrastructure architecture needed for teaching BI with a discussion of possible on-premises installations. It is followed by the description of cloud computing and its possibilities in teaching BI courses. We conclude with perceived difficulties in using cloud computing as a platform for teaching BI courses. Computing Requirements for BI Courses The typical computing infrastructure architecture needed for teaching BI is shown in Figure 1. It requires a database server to host source databases as well as the data warehouse. …

Data Mining for Health Care Professionals

In this paper, the authors discuss a data mining course that was offered for a cohort of health care professionals employed by a hospital consortium as an elective in a synchronous online MBA program. The students learned to use data mining to analyze data on two platforms, Enterprise Miner, SAS (2008) and XLMiner (an EXCEL add-in). The final assignment for the semester was for the students to analyze a data set from their place of employment. This paper describes the projects and resulting benefits to the companies for which the students worked.