Data Objects and Attribute Types

Note that quantitative attributes can be integer-valued or continuous. – Numeric operations such as mean standard deviation are meaningful. Data Mining.

Mining Quantitative Association Rules in a Atherosclerosis Dataset

Keywords: data mining association rule

Optimal Subgroup Discovery in Purely Numerical Data

27 janv. 2021 Mining purely numerical data is quite popular. It concerns data made of objects described by numerical attributes and one of these attributes ...

DB-HReduction: A Data Preprocessing Algorithm for Data Mining

time the data are collected without “mining” in mind. In addition

1 CLUSTERING LARGE DATA SETS WITH MIXED NUMERIC AND

Another characteristic is that data in data mining often contains both numeric and categorical values. The traditional way to treat categorical attributes as

Numerical Association Rule Mining from a Defined Schema Using

2 juil. 2021 Keywords: association rules; data mining; ... encompasses numerical attributes in the search process for patterns through rules in the data.

Mining Optimized Association Rules for Numeric Attributes

algorithms that compute the optimized ranges in linear time if the data are sorted. Since sorting data with respect to each numeric attribute is.

LATEX-Numeric: Language Agnostic Text Attribute Extraction for

11 juin 2021 We rely on dis- tant supervision for training data generation removing dependency on manual labels. One issue with distant supervision is that ...

Optimal Subgroup Discovery in Purely Numerical Data

27 janv. 2021 Mining purely numerical data is quite popular. It concerns data made of objects described by numerical attributes and one of these attributes ...

1992-ChiMerge: Discretization of Numeric Attributes

Many classification algorithms require that the training data contain only discrete attributes. To use such an algorithm when there are numeric at-.

Data Mining and Machine Learning: Fundamental Concepts and

Chapter 2: Numeric Attributes Zaki & Meira Jr (RPI and UFMG) Data Mining and Machine Learning Chapter 2: Numeric Attributes 1/35 Univariate Analysis Univariate analysis focuses on a single attribute at a time The data matrix D is an n×1 matrix D = X x 1 x 2 x n where X is the numeric attribute of interest with x

Describe the different types of attributes one may come across in a

01/27/2021 Introduction to Data Mining 2nd Edition 18 Tan Steinbach Karpatne Kumar Data Matrix ˜ If data objects have the same fixed set of numeric attributes then the data objects can be thought of as points in a multi-dimensional space where each dimension represents a distinct attribute

Data Mining and Analysis - Cambridge

numeric attribute is one that has a real-valued or integer-valued domain ForexampleAgewithdomain(Age) =NwhereNdenotes the set of natural numbers(non-negative integers) is numeric and so is petal length in Table 1 1 withdomain(petal length)=R+(the set of all positive real numbers)

Data Mining - University of Waikato

We will focus on nominal and numeric ones Data Mining: Practical Machine Learning Tools and Techniques (Chapter 2) 4 What’s a concept? Styles of learning: Classification learning: predicting a discrete class Association learning: detecting associations between features Clustering: grouping similar instances into clusters

Data Mining: Data - Khoury College of Computer Sciences

There are different types of attributes –Nominal uExamples: ID numbers eye color zip codes –Ordinal uExamples: rankings (e g taste of potato chips on a scale from 1-10) grades height in {tall medium short} –Interval uExamples: calendar dates temperatures in Celsius or Fahrenheit –Ratio

Searches related to numeric attributes in data mining filetype:pdf

There are a variety of statistical techniques available to analyse quantitative (numeric) data sets In this case we have selected to use Principal Components Analysis (PCA) to reduce the dimensionality of our data and Growing Neural Gas (GNG) to identify potentially interesting clusters of data

[PDF] Data Objects and Attribute Types • Basic Statistical Descriptions of

A collection of attributes describe an object • Attribute values are numbers or symbols assigned to an attribute Data Mining

[PDF] Data Lecture Notes for Chapter 2 Introduction to Data Mining 2nd

27 jan 2021 · Introduction to Data Mining 2nd Edition Tan Steinbach Karpatne Kumar Attribute Values Attribute values are numbers or symbols

[PDF] Data Mining - University of Waikato

Attributes: measuring aspects of an instance We will focus on nominal and numeric ones 4 Data Mining: Practical Machine Learning Tools and Techniques

[PDF] Data Mining

There are different types of attributes – Nominal:Examples: ID numbers eye color zip codes – Ordinal: Examples: rankings (e g taste of potato

[PDF] Data Mining Input: Concepts Instances Attributes and Pre

Numeric attributes have values that come from a range of numbers attribute possible values Body Temp any value in 96 0-106 0 Salary any value in $15000

[PDF] Basic Data Mining Techniques

Attributes Objects Data Mining Lecture 2 4 Attribute Values • Attribute values are numbers or symbols assigned to an attribute

[PDF] Know Your Data

In our presentation we have organized attributes into nominal binary ordinal and numeric types There are many ways to organize attribute types The types

[PDF] Data Chapter 2 Introduction to Data Mining

Data Mining: Data Chapter 2 Attribute values are numbers or symbols assigned to an attribute Different attributes can be mapped to the same set of

[PDF] 22 Chapter 2 Data

In turn data objects are described by a number of attributes that capture the basic characteristics of an object such as the mass of a physical object or the

[PDF] LECTURE NOTES ON DATA MINING& DATA WAREHOUSING

A user does not want hundreds of pages of numeric results He does not understand them; he cannot summarize interpret and use them for successful decision

What are the different types of attributes in data mining?

Describe the different types of attributes one may come across in a data mining data set with two examples of each type. The values of a nominal attribute are just different names, i.e. nominal attributes provide only enough information to distinguish one object from another (=,?) Examples: zip codes, employees ID numbers.

What are the characteristics of a data mining algorithm?

Data mining algorithms are often sensitive to specific characteristics of the data: outliers (data values that are very different from the typical values in your database), irrelevant columns, columns that vary together (such as age and date of birth), data coding, and data that you choose to include or exclude.

What is attribute importance in Oracle Data Mining?

Oracle Data Mining supports the Attribute Importance mining function, which ranks attributes according to their importance in predicting a target. Attribute importance does not actually perform feature selection since all the predictors are retained in the model.

What is a numeric attribute?

A numeric attribute is quantitative; that is, it is a measurable quantity, represented in integer or real values. Numeric attributes can be interval-scaled or ratio-scaled. Photo by Luke Chesseron Unsplash What are interval-scaled attributes? A temperature attribute is interval-scaled.

Getting to Know Your Data

Data Mining1

Basic Statistical Descriptions of Data

Measuring Data Similarity and Dissimilarity

Data Mining2

Data Objects and Attribute Types

Basic Statistical Descriptions of Data

Measuring Data Similarity and Dissimilarity

Data-Related Issues

for Successful Data Mining

Type of Data:

Data sets differ in a number of ways.

Type of data determines which techniques can be used to analyze the data.

Quality of Data:

Data is often far from perfect.

Improving data quality improves the quality of the resulting analysis. Preprocessing Steps to Make Data More Suitable for Data Mining: Raw data must be processed in order to make it suitable for analysis.

Improve data quality,

Modify data so that it better fits a specified data mining technique.

Analyzing Data in Terms of its Relationships:

find relationships among data objects and then perform remaining analysis using these relationships rather than data objects themselves. There are many similarity or distance measures, and the proper choice depends on the type of data and application.

Data Mining3

What is Data?

Data sets are made up of data objects.

A data object represents an entity.

Also called sample, example, instance, data point, object, tuple.

Data objects are described by attributes.

An attributeis a property or characteristic of a data 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.

Attribute values are numbers or symbols assigned to an attribute.

Data Mining4

A Data Object

database rowsÎdata objects database columns Îattributes

Data Mining5

Attributes

Attribute(or dimensions, features, variables): a data field, representing a characteristic or feature of a data object.

E.g., customer _ID, name, address

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

Data Mining6

Attribute Types

Four main types of attributes

Nominal: Categorical (Qualitative)

Hair color, marital status, occupation, ID numbers, zip codes

An important nominal attribute: Binary

Nominal attribute with only 2 states (0 and 1)

Ordinal:Categorical (Qualitative)

Values have a meaningful order (ranking) but magnitude between successive values is not known. Size = {small, medium, large}, grades, army rankings

Interval:Numeric (Quantitative)

Measured on a scale of equal-sized units

Values have order:

temperature in C

No true zero-point: ratios are not meaningful

Ratio:Numeric (Quantitative)

Inherent zero-point: ratios are meaningful

temperature in Kelvin, length, counts, monetary quantities

Data Mining7

Attribute Types

Four main types of attributes: Nominal Attributes

The values of a nominal attribute are symbols or names of things. Each value represents some kind of category, code, or state, Nominal attributes are also referred to as categorical attributes. The values of nominal attributes do not have any meaningful order. Example: The attribute marital_status can take on the values single, married, divorced, and widowed. Because nominal attribute values do not have any meaningful order about them and they are not quantitative. It makes no sense to find the mean (average) value or median (middle) value for such an attribute. mode).

Data Mining8

Attribute Types

Four main types of attributes: Nominal Attributes

A binary attribute is a special nominal attribute with only two states: 0 or 1. A binary attribute is symmetricif both of its states are equally valuable and carry the same weight. Example: the attribute genderhaving the states maleand female. A binary attribute is asymmetricif the outcomes of the states are not equally important. Example: Positive and negativeoutcomes of a medical test for HIV. By convention, we code the most important outcome, which is usually the rarest one, by 1 (e.g., HIV positive) and the other by 0 (e.g., HIV negative).

Data Mining9

Attribute Types

Four main types of attributes: Ordinal Attributes

An ordinal attribute is an attribute with possible values that have a meaningful order or ranking among them, but the magnitude between successive values is not known. Example: An ordinal attribute drink_size corresponds to the size of drinks available at a fast-food restaurant. This attribute has three possible values: small, medium, and large. The values have a meaningful sequence (which corresponds to increasing drink size); however, we cannot tell from the values how much bigger, say, a medium is than a large. The central tendency of an ordinal attribute can be represented by its modeand its median(middle value in an ordered sequence), but the meancannot be defined.

Data Mining10

Attribute Types

Four main types of attributes: Interval Attributes Interval attributes are measured on a scale of equal-size units. We can compare and quantify the difference between values of interval attributes. Example: A temperatureattribute is an interval attribute. We can quantify the difference between values. For example, a temperature of 20oC is five degrees higher than a temperature of 15oC. Temperatures in Celsius do not have a true zero-point, that is, 0o Although we can compute the difference between temperature values, we cannot talk of one temperature value as being a multiple of another. Without a true zero, we cannot say, for instance, that 10oC is twice as warm as 5oC . That is, we cannot speak of the values in terms of ratios. The central tendency of an interval attribute can be represented by its mode, its median(middle value in an ordered sequence), and its mean.

Data Mining11

Attribute Types

Four main types of attributes: Ratio Attributes

A ratio attribute is a numeric attribute with an inherent zero-point. Example: A number_of_wordsattribute is a ratio attribute. If a measurement is ratio-scaled, we can speak of a value as being a multiple (or ratio) of another value. The central tendency of an ratio attribute can be represented by its mode, its median (middle value in an ordered sequence), and its mean.

Data Mining12

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

Data Mining13

Properties of Attribute Values

Data Mining14

Attribute

Type

DescriptionExamples

NominalThe 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}

OrdinalThe values of an ordinal attribute provide

enough information to order objects. (<, >) hardness of minerals, {good, better, best}, grades, street numbers

IntervalFor interval attributes, the differences

between values are meaningful, i.e., a unit of measurement exists. (+, -) calendar dates, temperature in Celsius or Fahrenheit RatioFor ratio variables, both differences and ratios are meaningful. (*, /) temperature in Kelvin, monetary quantities, counts, age, mass, length,

Attribute Types

Categorical (Qualitative) and Numeric (Quantitative) Nominaland Ordinalattributes are collectively referred to as categorical or qualitative attributes. qualitative attributes, such as employee ID, lack most of the properties of numbers. Even if they are represented by numbers, i.e. , integers, they should be treated more like symbols .

Meanof values does not have any meaning.

Intervaland Ratioare collectively referred to as quantitative or numeric attributes. Quantitative attributes are represented by numbers and have most of the properties of numbers . Note that quantitative attributes can be integer-valued or continuous. Numeric operations such as mean, standard deviation are meaningful

Data Mining15

Discrete vs. Continuous Attributes

Discrete Attribute

Has only a finite or countably infinite set of values zip codes, profession, or the set of words in a collection of documents

Sometimes, represented as integer variables

Note: Binary attributes are a special case of discrete attributes Binary attributes where only non-zero values are important are called asymmetric binary attributes.

Continuous Attribute

Has real numbers as attribute values

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 Mining16

Types of data sets

Data Mining17

Record

Relational records

Data matrix, e.g., numerical matrix,

crosstabs

Document data: text documents:

quotesdbs_dbs20.pdfusesText_26