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File: Data Preprocessing In Data Mining Pdf 179257 | Datppt
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 ...

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                            INTRODUCTION TO DATA                              - U
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                                          MINING:                             - J
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                              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 
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                       –    Ordinal                                                                                                 a
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                                  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
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                                  Examples: calendar dates, temperatures in Celsius or                                             F
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                                   Fahrenheit.                                                                                       - J
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                       –    Ratio                                                                                                    2012
                                  Examples: temperature in Kelvin, length, time, counts                                             
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                  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
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                         –    Often represented as integer variables.                                                                      M
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                         –    Note: binary attributes are a special case of discrete attributes   ni
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                     Continuous Attribute                                                                                                 - U
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                         –    Has real numbers as attribute values                                                                        P
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                         –    Examples: temperature, height, or weight.                                                                    - J
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                         –    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.   
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...D a t m i ni ng cours e introduction to data u f p mining j une preprocessing chiara renso kdd lab isti cnr pisa italy it what is collection of objects and attributes their an attribute property or characteristic object examples eye color person temperature etc also known as variable field feature describe record point case sample entity instance types there are different nominal id numbers zip codes ordinal rankings g taste potato chips on scale from grades height in tall medium short interval calendar dates temperatures celsius fahrenheit ratio kelvin length time counts discrete continuous has only finite countably infinite set values the words documents often represented integer variables note binary special real weight practically can be measured using number digits typically floating...

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