This presentation discusses the importance of data mining for business intelligence and presents a supervised approach to it. It covers techniques such as visualization, association rule, and neural network. The report emphasizes the need for accuracy and relevance in decision making.

Assignment 3: Oral Presentation

This assignment is an oral presentation of your technical report in Assignment 2. The presentation should be a power point file with voice embedded in each slide, with a maximum of 15 slides and a maximum duration of 15 minutes.

Learn about the supervised approach to data mining for business intelligence and its importance in decision making. Explore techniques such as visualization, association rule, and neural network.

Data Mining for Business Intelligence: A Supervised Approach

Enterprise Business Intelligence

This paper discusses the implementation of supervised data mining in business intelligence for growth and analysis of managerial roles in companies. It covers statistical-based approaches, graph clustering, and Markov Cluttering. The paper also includes a literature review, a summary of the dataset, and a demonstration of the evaluation process. The techniques used include visualization, association rule, and neural network techniques.

Hartpury University and Hartpury College

Data Science and Big Data

Artificial Intelligence

Implementation of Supervised Data Mining in Business Intelligence

Data  Mining  Report  2022

Report

Introduction to Natural language processing

This paper presents a critical review of five articles on the POS tagging algorithm for location mining from tweets. It discusses the tools available for analyzing social networking data, such as Keyhole and Tweet Sentiment Visualization. It also explores the use of data crawlers and sentiment analysis techniques. The paper concludes by evaluating and comparing different algorithms for each analysis component.

STI West Negros University

POS Tagging Algorithm for Location Mining from Tweets

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.

Statistics and Probability

Implementing Machine Learning and Data Mining Techniques for Business Intelligence

The huge production of data day to day has led to a revolution

Assignment

This presentation explores the applications of business intelligence analytics and data mining in the banking industry for decision making. It discusses the techniques used in the banking industry, such as association, clustering, forecasting, and classification. It also highlights the challenges faced in implementing data mining and business analytics in the banking industry.

Data Mining for Business Intelligence: A Supervised Approach

Implementation of Supervised Data Mining in Business Intelligencelg...

Data Mining Report 2022lg...

POS Tagging Algorithm for Location Mining from Tweetslg...

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

The huge production of data day to day has led to a revolutionlg...

Business Analytics and Data Mining Techniques for Banking Industrylg...

Implementation of Supervised Data Mining in Business Intelligence

Data Mining Report 2022

POS Tagging Algorithm for Location Mining from Tweets

Implementing Machine Learning and Data Mining Techniques for Business Intelligence

The huge production of data day to day has led to a revolution

Business Analytics and Data Mining Techniques for Banking Industry