TAILIEUCHUNG - Data Mining Classification: Alternative Techniques - Lecture Notes for Chapter 5 Introduction to Data Mining

Mutually exclusive rules Classifier contains mutually exclusive rules if the rules are independent of each other Every record is covered by at most one rule Exhaustive rules Classifier has exhaustive coverage if it accounts for every possible combination of attribute values Each record is covered by at least one rule | Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining Rule-Based Classifier Classify records by using a collection of “if then ” rules Rule: (Condition) y where Condition is a conjunctions of attributes y is the class label LHS: rule antecedent or condition RHS: rule consequent Examples of classification rules: (Blood Type=Warm) (Lay Eggs=Yes) Birds (Taxable Income © Tan,Steinbach, Kumar Introduction to Data Mining Rule-Based Classifier Classify records by using a collection of “if then ” rules Rule: (Condition) y where Condition is a conjunctions of attributes y is the class label LHS: rule antecedent or condition RHS: rule consequent Examples of classification rules: (Blood Type=Warm) (Lay Eggs=Yes) Birds (Taxable Income Bird The rule R3 covers the grizzly bear => Mammal Rule Coverage and Accuracy Coverage of a rule: Fraction of records that satisfy the antecedent of a rule Accuracy of a rule: Fraction of records that satisfy both the antecedent and consequent of a rule (Status=Single) No Coverage = 40%, Accuracy = 50% How does Rule-based Classifier Work? R1: (Give Birth = no) (Can Fly = yes) Birds R2: (Give Birth = no) (Live in Water = yes) Fishes R3: (Give Birth = yes) (Blood Type = warm) Mammals R4: (Give Birth = no) (Can Fly = no) Reptiles R5: (Live in Water = sometimes) Amphibians A lemur triggers rule R3, so it is classified as a mammal A turtle triggers both R4 and R5 A dogfish shark triggers none of the rules Characteristics of .

• 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