This presentation discusses the development of a Knowledge Map Management System using techniques of Data Mining and Information Retrieval. It aims to manage the dynamic knowledge of various communities on the internet. The authors propose features like Knowledge Navigation, Advisory Learning, and Knowledge Seeking. The presentation evaluates knowledge flow patterns of data and presents the knowledge among others effectively. The research methodology includes the Recall and Precision method, and three strategies have been implemented for conducting the research. The presentation also includes a literature review and references.

Knowledge map creation and maintenance for virtual communities of practice

This paper proposes a knowledge map management system to facilitate knowledge management in virtual communities of practice. The system utilizes information retrieval and data mining techniques to create and maintain knowledge maps. The effectiveness of the system is evaluated by domain experts, who find the automatically created knowledge maps to be of high quality with minimal modifications required. The system allows for the efficient creation and maintenance of knowledge maps in virtual communities of practice.

A system using Data Mining and Information Retrieval techniques presents knowledge flow patterns effectively.

Knowledge Map Management System: A Literature Review

This assignment explores the application of the K-means clustering technique in Northwell Healthcare Organization. It focuses on utilizing K-means for data mining purposes, analyzing patient information such as demographics, medical history, and financial status. The extracted data is then clustered based on patient needs to provide insights into their current health condition and guide treatment decisions. Additionally, the assignment examines how K-means can be used for document clustering within the organization, classifying documents according to departments for efficient management and issue identification.

Data Science and Big Data

K-Means Clustering in Healthcare

Quantitative Methods

This research proposal aims to explore and evaluate the impact of process mining on learning analytics and investigate its benefits and challenges. The proposal discusses the role of process mining in digital transformation and its techniques in process management. It also explains the concept of learning analytics and its steps to optimize the learning process and environment. The proposal includes a literature review on process mining and learning analytics, highlighting the importance of these techniques in improving performance and learning quality. The methodology section outlines the problem statement, research questions, and experimental method to evaluate the impact of process mining on learning analytics. The proposal concludes with references to relevant studies on process mining and learning analytics.

University of Sydney

Process Mining in Learning Analytics: Benefits and Challenges

This project aims to implement machine learning and data mining techniques for business intelligence using the Weka application. The dataset is analyzed using 10 different data mining techniques, and the performance is evaluated through practical assignments and visualizations. The clustering technique of K-means is implemented, and the data is visualized using classifiers. The dataset is also analyzed using content-based filtering and time series forecasting. The subject, course code, course name, and college/university are not mentioned.

Artificial Intelligence

Statistics and Probability

Implementing Machine Learning and Data Mining Techniques for Business Intelligence

Hartpury University and Hartpury College

Political Science

Sustainable development as a Competitive Advantage | Construction

Introduction to Data Mining

University of Southeastern Philippines

DATA MINING. I. NTRODUCTION. The concept of data mining

Presentation

This project aims to analyze the real-time business situation by applying data mining techniques to the Behavioral Risk Factor Surveillance System data. The project identifies the behavioral risk factors for surveillance system by analyzing the nutrition, obesity, and physical activity by using the Naive Bayes classifier. The project discusses the issues with the Naive Bayes classifier and suggests using effective rules and trees data mining algorithms for future analysis.

Knowledge Map Management System: A Literature Review

End of preview

K-Means Clustering in Healthcare

Process Mining in Learning Analytics: Benefits and Challenges

Implementing Machine Learning and Data Mining Techniques for Business Intelligence

Sustainable development as a Competitive Advantage | Construction

DATA MINING. I. NTRODUCTION. The concept of data mining

Data Mining Techniques for Analysis of Nutrition, Physical Activity and Obesity from Behavioral Risk Factor Surveillance System Data

Knowledge Map Management System: A Literature Review

End of preview

K-Means Clustering in Healthcarelg...

Process Mining in Learning Analytics: Benefits and Challengeslg...

Implementing Machine Learning and Data Mining Techniques for Business Intelligencelg...

Sustainable development as a Competitive Advantage | Constructionlg...

DATA MINING. I. NTRODUCTION. The concept of data mininglg...

Data Mining Techniques for Analysis of Nutrition, Physical Activity and Obesity from Behavioral Risk Factor Surveillance System Datalg...

K-Means Clustering in Healthcare

Process Mining in Learning Analytics: Benefits and Challenges

Implementing Machine Learning and Data Mining Techniques for Business Intelligence

Sustainable development as a Competitive Advantage | Construction

DATA MINING. I. NTRODUCTION. The concept of data mining

Data Mining Techniques for Analysis of Nutrition, Physical Activity and Obesity from Behavioral Risk Factor Surveillance System Data