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Lecture3 - CSC 5800 Intelligent Systems Algorithms and...

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1 Lecture 3: Data CSC 5800: Intelligent Systems: Algorithms and Tools Acknowledgement: This lecture is partially based on the slides from Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, “ Introduction to Data Mining”, Addison-Wesley (2005). Today’s Lecture….. Attributes and objects Types of Data Sets Data Quality issues
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2 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 Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes Attributes Objects Attribute Values Attribute values are numbers or symbols assigned to an attribute Attribute is a characteristic/feature/property. 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
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3 Types of Attributes – Nominal Examples: ID numbers, eye color, zip codes – Ordinal Examples: rankings (e.g., 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
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4 Attribute Type Description Examples Operations Nominal The values of a nominal attribute are just different names, i.e., nominal attributes provide only enough information to distinguish one object from another. (=, ) zip codes, employee ID numbers, eye color, sex: { male, female } mode, entropy, contingency correlation, χ 2 test Ordinal The values of an ordinal attribute provide enough information to order objects. (<, >) hardness of minerals, { good, better, best }, grades, street numbers median, percentiles, rank correlation, run tests, sign tests
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