tailieunhanh - Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining

Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc. Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object Object is also known as record, point, case, sample, entity, or instance | Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar What is Data? Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc. Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object Object is also known as record, point, case, sample, entity, or instance Attributes Objects Attribute Values Attribute values are numbers or symbols assigned to an attribute Distinction between attributes and attribute values Same attribute can be mapped to different attribute values Example: height can be measured in feet or meters Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are integers But properties of attribute values can be different ID has no limit but age has a maximum and minimum value Measurement of Length The way you measure an attribute | Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar What is Data? Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc. Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object Object is also known as record, point, case, sample, entity, or instance Attributes Objects Attribute Values Attribute values are numbers or symbols assigned to an attribute Distinction between attributes and attribute values Same attribute can be mapped to different attribute values Example: height can be measured in feet or meters Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are integers But properties of attribute values can be different ID has no limit but age has a maximum and minimum value Measurement of Length The way you measure an attribute is somewhat may not match the attributes properties. Types of Attributes There are different types of attributes Nominal Examples: ID numbers, eye color, zip codes Ordinal Examples: rankings (., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short} Interval Examples: calendar dates, temperatures in Celsius or Fahrenheit. Ratio Examples: temperature in Kelvin, length, time, counts Properties of Attribute Values The type of an attribute depends on which of the following properties it possesses: Distinctness: = Order: Addition: + - Multiplication: * / Nominal attribute: distinctness Ordinal attribute: distinctness & order Interval attribute: distinctness, order & addition Ratio attribute: all 4 properties Attribute Type Description Examples Operations Nominal The values of a nominal attribute are just different names, ., nominal attributes provide only enough information to distinguish one object from another. (=, ) zip codes, employee ID .

• 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