27 jan 2021 · There are different types of attributes – Nominal ◇ Examples: ID numbers, eye color, zip codes – Ordinal ◇ Examples: rankings (e g , taste of
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Data Mining Lecture 2 5 Types of Attributes • There are different types of attributes – Nominal • Examples: ID numbers, eye color, zip codes – Ordinal
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Ordinal An order preserving change of values, i e , new_value = f(old_value) where f is a monotonic function An attribute encompassing the notion of good, better best can be represented equally well by the values {1, 2, 3} or by { 0 5, 1, 10} Ratio new_value = a * old_value Length can be measured in meters or feet
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Data Mining: Practical Machine Learning Tools and Techniques (Chapter 2) Nominal, ordinal, interval, ratio Preparing the input ARFF, attributes, missing values, getting to know data Instances: the individual, independent examples of
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Data Mining 14 Attribute Type Description Examples Nominal The values of a nominal attribute are just different names, i e , nominal attributes provide only
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Example: Attribute values for ID and age are integers Ordinal attribute: distinctness order Data mining example: a classification model for detecting
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Data Objects and Attributes □ Datasets are made up of data objects □ A data object ( or sample , example, instance, data point, tuple) represents an entity
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It's tempting to jump straight into mining, but first, we need to get the data ready clude nominal attributes, binary attributes, ordinal attributes, and numeric For example, suppose we have a database where the data objects are patients,
Qualitative data consist of labels, features, non- the attribute is represented by ordinal variable, Example of numeric variables: analysis • Ratios between numbers on an interval scale are not meaningful (e g 90° is not twice as hot as
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Also called samples , examples, instances, data points, objects, tuples Q1: Is student ID a nominal, ordinal, or numerical attribute? □ Q2: What about eye color
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Preprocessing Steps to Make Data More Suitable for Data Mining: Example: An ordinal attribute drink_size corresponds to the size of drinks available at.
What's in an example? Relations flat files
There are different types of attributes. – Nominal:Examples: ID numbers eye color
27 janv. 2021 What is Data? Collection of data objects and their attributes. An attribute is a property or characteristic of an object. – Examples: eye ...
3 janv. 2020 Abstract—Ordinal data are common in many data mining and ... and ordinal attributes is common
Also called samples examples
What's in an example? Relations flat files
Keywords: attribute evaluation ordinal attributes
Introduction to Data Mining. 1/2/2009. 4. Different attributes can be mapped to the same set of values. ? Example: Attribute values for ID and age are
Basic Data Mining Techniques. Data Mining Lecture 2 Example: Attribute values for ID and age are integers ... Ordinal attribute: distinctness & order.
CHAPTER 1Data Mining and Analysis Data mining is the process of discovering insightful interesting and novel patterns as well as descriptive understandable and predictive models from large-scale data We begin this chapter by looking at basic properties of data modeled as a data matrix
The type of an attribute depends on which of the following properties/operations it possesses: Distinctness: = Order: < > Differences are+ -meaningful : Ratios are * /meaningful Nominal attribute: distinctness Ordinal attribute: distinctness & order Interval attribute: distinctness order & meaningful differences
Data Mining Classification: Basic Concepts Decision Trees and Model Evaluation Data Mining Classification: Basic Concepts Decision Trees and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining by Tan Steinbach Kumar © TanSteinbach Kumar Introduction to Data Mining 4/18/2004 1 © TanSteinbach Kumar
2/08/2021 Introduction to Data Mining 2 nd Edition 11 Estimate Probabilities from Data • For continuous attributes: – Discretization: Partition the range into bins: Replace continuous value with bin value – Attribute changed from continuous to ordinal – Probability density estimation: Assume attribute follows a normal distribution
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 5 6
Ordinal attributes are also called “numeric” or “continuous” Preparing the input • No quality data no quality mining results! • Quality decisions must be based on quality data • Data extraction integration transformation cleaning and reduction comprise the majority
What is a continuous attribute in data mining?
• Continuous Attribute – Has real numbers as attribute values – Examples: temperature, height, or weight – Practically, real values can only be measured and represented using a finite number of digits – Continuous attributes are typically represented as floating- point variables Data Mining Lecture 2 10
Can nominal values be ordinal?
If numbers are used as IDs or names of categories,the corresponding attribute is actually nominal. Note that it doesn't make sense to order the values of such attributes. Also note that some nominal values canbe ordinal: ? Distinction between nominal and ordinal not always clear (e.g. attribute “outlook” – is there an ordering?)
What are some examples of data mining?
• 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 Data Mining Lecture 2 6
What is an example of an ordinal scale?
– Ordinal • Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short} – Interval
Data Objects and Attribute Types • Basic Statistical Descriptions of