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D a t a M i ni ng Cours e INTRODUCTION TO DATA - U F P E MINING: - J une DATA PREPROCESSING 2012 1 Chiara Renso KDD-LAB ISTI- CNR, Pisa, Italy chiara.renso@isti.cnr.it WHAT IS DATA? Collection of data objects and Attributes 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 Objects A collection of attributes describe an object – Object is also known as record, point, case, sample, entity, or instance TYPES OF ATTRIBUTES There are different types of attributes – Nominal Examples: ID numbers, eye color, zip codes D a t – Ordinal a M i Examples: rankings (e.g., taste of potato chips on a scale from ni 1-10), grades, height in {tall, medium, short} ng Cours – Interval e - U Examples: calendar dates, temperatures in Celsius or F P E Fahrenheit. - J une – Ratio 2012 Examples: temperature in Kelvin, length, time, counts 3 DISCRETE AND CONTINUOUS ATTRIBUTES Discrete Attribute – Has only a finite or countably infinite set of values – Examples: zip codes, counts, or the set of words in a collection of documents D a t a – Often represented as integer variables. M i – Note: binary attributes are a special case of discrete attributes ni ng Cours e Continuous Attribute - U F – Has real numbers as attribute values P E – Examples: temperature, height, or weight. - J une – Practically, real values can only be measured and represented 2012 using a finite number of digits. – Continuous attributes are typically represented as floating- point variables. 4
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