This course focuses on discovering relationships in transactional data through association rule mining techniques. Participants will learn how to apply Apriori and FP-Growth algorithms to identify patterns and correlations in datasets. Practical examples and hands-on labs ensure attendees gain the skills to perform market basket analysis and uncover actionable insights from transactional data.
Session 1: Introduction to Association Rules
Session 2: Apriori Algorithm
Session 1: FP-Growth Algorithm
Session 2: Applications of Association Rule Mining
We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.
This course provides an introduction to data mining, focusing on fundamental concepts, processes, and key applications.
This course provides practical training on preparing raw data for mining and analysis. Participants will learn techniques for handling missing values, identifying outliers, and selecting relevant features.
This course provides hands-on training in clustering techniques, including K-Means, DBSCAN, and hierarchical clustering.
This course provides hands-on training on building and evaluating predictive models using Python or R.
This course provides an in-depth exploration of text mining and natural language processing (NLP) techniques for extracting insights from unstructured text data.
This course offers practical training on using Hadoop and Spark for mining large-scale datasets.
This course focuses on methods for identifying outliers and unusual patterns in data.
This course focuses on effectively presenting data mining results using visualization tools like Tableau and Power BI.