[PDF] Basic Data Mining Techniques PDF

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.

Basic Data Mining Techniques

Data Mining Lecture 2 2

Overview

• Data & Types of Data • Fuzzy Sets • Information Retrieval • Machine Learning • Statistics & Estimation Techniques • Similarity Measures • Decision Trees

Data Mining Lecture 2 3

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

1 0

Attributes

Objects

Data Mining Lecture 2 4

Attribute Values

• 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 Mining Lecture 2 5

Types of Attributes

• There are different 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

Data Mining Lecture 2 6

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 2

Attribute

TypeDescriptionExamplesOperations

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}mode, entropy, contingency correlation,

χ2test

OrdinalThe 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

IntervalFor interval attributes, the differences between values are meaningful, i.e., a unit of measurement exists. (+, - )calendar dates, temperature in Celsius or Fahrenheitmean, standard deviation, Pearson's correlation, tand F

tests

RatioFor ratio variables, both differences and ratios are meaningful. (*, /)temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical currentgeometric mean, harmonic mean, percent variation

Attribute

LevelTransformationComments

NominalAny permutation of valuesIf all employee ID numbers were reassigned, would it make any difference?

OrdinalAn order preserving change of values, i.e., new_value = f(old_value) where fis 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}.

Intervalnew_value =a * old_value + b where a and b are constantsThus, the Fahrenheit and Celsius temperature scales differ in terms of where their zero value is and the size of a unit (degree).

Rationew_value = a * old_valueLength can be measured in meters or feet.

Data Mining Lecture 2 9

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 - Often represented as integer variables. - Note: binary attributes are a special case of discrete attributes • 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

Types of data sets

• Record - Data Matrix - Document Data - Transaction Data • Graph - World Wide Web - Molecular Structures • Ordered - Spatial Data - Temporal Data - Sequential Data - Genetic Sequence Data

Data Mining Lecture 2 11

Characteristics of Structured Data

• Dimensionality - Curse of Dimensionality • Sparsity - Only presence counts • Resolution - Patterns depend on the scale

Data Mining Lecture 2 12

Record Data

• Data that consists of a collection of records, each of which consists of a fixed set of attributes

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

10 3

Data Mining Lecture 2 13

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 • Such data set can be represented by an m by n matrix, where there are m rows, one for each object, and n columns, one for each attribute

1.12.216.226.2512.651.22.715.225.2710.23Thickness LoadDistanceProjection

of y loadProjection of x Load

1.12.216.226.2512.651.22.715.225.2710.23Thickness LoadDistanceProjection

of y loadProjection of x Load

Data Mining Lecture 2 14

Document Data

• Each document becomes a `term" vector, - each term is a component (attribute) of the vector, - the value of each component is the number of times the corresponding term occurs in the document.

Document 1

seasontimeout lostwi ngamescoreballpla ycoachteam

Document 2

Document 3

3050260202

0 0

702100300

100122030

Data Mining Lecture 2 15

Transaction Data

• A special type of record data, where - each record (transaction) involves a set of items. - For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.

TID Items

1 Bread, Coke, Milk

2 Beer, Bread

3 Beer, Coke, Diaper, Milk

4 Beer, Bread, Diaper, Milk

quotesdbs_dbs20.pdfusesText_26

[PDF] [PDF] Basic Data Mining Techniques

What are the different types of attributes in data mining?

What are the characteristics of a data mining algorithm?

What is attribute importance in Oracle Data Mining?

What is a numeric attribute?

Basic Data Mining Techniques

Data Mining Lecture 2 2

Overview

Data Mining Lecture 2 3

What is Data?

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

Data Mining Lecture 2 4

Attribute Values

Data Mining Lecture 2 5

Types of Attributes

Fahrenheit.

Data Mining Lecture 2 6

Properties of Attribute Values

Attribute

TypeDescriptionExamplesOperations

ID numbers, eye color,

χ2test

Attribute

LevelTransformationComments

Data Mining Lecture 2 9

Discrete and Continuous Attributes

Data Mining Lecture 2 10

Types of data sets

Data Mining Lecture 2 11

Characteristics of Structured Data

Data Mining Lecture 2 12

Record Data

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

Data Mining Lecture 2 13

Data Matrix

1.12.216.226.2512.651.22.715.225.2710.23Thickness LoadDistanceProjection

1.12.216.226.2512.651.22.715.225.2710.23Thickness LoadDistanceProjection

Data Mining Lecture 2 14

Document Data

Document 1

Document 2

Document 3

3050260202

702100300

100122030

Data Mining Lecture 2 15

Transaction Data

TID Items

1 Bread, Coke, Milk

2 Beer, Bread

3 Beer, Coke, Diaper, Milk

4 Beer, Bread, Diaper, Milk