TAILIEUCHUNG - Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining

Transform categorical attribute into asymmetric binary variables Introduce a new “item” for each distinct attribute-value pair Example: replace Browser Type attribute with Browser Type = Internet Explorer Browser Type = Mozilla Browser Type = Mozilla | Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining Continuous and Categorical Attributes Example of Association Rule: {Number of Pages [5,10) (Browser=Mozilla)} {Buy = No} How to apply association analysis formulation to non-asymmetric binary variables? Handling Categorical Attributes Transform categorical attribute into asymmetric binary variables Introduce a new “item” for each distinct attribute-value pair Example: replace Browser Type attribute with Browser Type = Internet Explorer Browser Type = Mozilla Browser Type = Mozilla Handling Categorical Attributes Potential Issues What if attribute has many possible values Example: attribute country has more than 200 possible values Many of the attribute values may have very low support Potential solution: Aggregate the low-support attribute values What if distribution of attribute .

• Cơ sở dữ liệu
• Data Mining
• Alternative Techniques
• training data
• Simple models
• Logistic regression
• Decision trees
• Data warehousing and data mining
• Data warehousing
• Data warehouse modelling
• Data mining functionalities
• Association rule mining
• FP growth algorithm
• K Nearest neighbor classification
• Lecture Data mining
• Bài giảng Khai thác dữ liệu
• Data mining for data streams
• Mining data streams
• Stream frequent pattern analysis
• Khai phá dữ liệu
• Overview of data mining
• Association rules
• Other Data Mining problems
• Applications of data mining
• Sequence data
• Sequential pattern mining
• Sequential pattern mining algorithms
• Data stream mining
• Data stream processing algorithms
• Cash register model
• Time series model
• Lecture Data mining and analysis
• Khai thác dữ liệu
• Data analysis foundations
• Frequent pattern mining
• Itemset mining
• Sequence mining
• Graph pattern mining
• Data warehouses
• On line analytical processing
• Association rules mining
• Mining association rules
• Computational complexity
• Frequent itemset generation
• Mining frequent closed patterns
• Data warehouse
• Data warehouse architecture
• Data warehouse implementation
• Data warehouse—subject oriented
• Graph data
• High dimensional data
• Numeric attributes
• Categorical attributes
• Kernel methods
• Dimensionality reduction
• Summarizing itemsets
• Rule assessment