同济大学：《大数据分析与数据挖掘 Big Data Analysis and Mining》课程教学资源（PPT课件讲稿）Getting to Know Your Data

Big Data Analysis and Mining Lecture 2: Getting to Know your Data Weixiong Rao饶卫雄 Tongji University同济大学软件学院 2015 Fall wxrao@tongji.edu.cn 2021/2/9 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

2021/2/9 1 Big Data Analysis and Mining Lecture 2: Getting to Know Your Data Weixiong Rao 饶卫雄 Tongji University 同济大学软件学院 2015 Fall wxrao@tongji.edu.cn

Getting to Know your Data Data Objects and Attribute Types (1 a Basic Statistical Descriptions of data Data visualization Measuring Data Similarity and Dissimilarity a Summary 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

2 Getting to Know Your Data ◼ Data Objects and Attribute Types ◼ Basic Statistical Descriptions of Data ◼ Data Visualization ◼ Measuring Data Similarity and Dissimilarity ◼ Summary

Types of Data Sets ■ Record ◆ Relational records Data matrix, e.g., numerical matrix, crosstabs Document data: text documents term frequency vector Document 1 ◆ Transaction data Graph and network Document 2 0 ◆ World wide Web 003 Document 3 00 2 Social or information networks Molecular structures Ordered TD tems Video data: sequence of images Bread. Coke. Milk Temporal data: time-series Beer. bread Sequential Data transaction sequences Genetic Beer, Coke, Diaper, Milk I Spatial, image and multimedia Beer, Bread, Diaper, Milk Spatial data: maps Coke, Diaper, Milk Image data Video data 同济大学软件学院 ool of Software Engineering. Tongpi Unversity 3

3 Types of Data Sets ◼ Record ◆ Relational records ◆ Data matrix, e.g., numerical matrix, crosstabs ◆ Document data: text documents: term - frequency vector ◆ Transaction data ◼ Graph and network ◆ World Wide Web ◆ Social or information networks ◆ Molecular Structures ◼ Ordered ◆ Video data: sequence of images ◆ Temporal data: time-series ◆ Sequential Data: transaction sequences ◆ Genetic sequence data ◼ Spatial, image and multimedia: ◆ Spatial data: maps ◆ Image data: ◆ Video data: D o c u m e n t 1 season timeout lost wi n game score ball pla y coach team D o c u m e n t 2 D o c u m e n t 3 3 0 5 0 2 6 0 2 0 2 00 7 0 2 1 0 0 3 0 0 1 0 0 1 2 2 0 3 0 TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk

Important Characteristics of Structured Data a Dimensionality Curse of dimensionality a Sparsity Only presence counts ■ Resolution Patterns depend on the scale ■ Distribution Centrality and dispersion 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

4 Important Characteristics of Structured Data ◼ Dimensionality ◆ Curse of dimensionality ◼ Sparsity ◆ Only presence counts ◼ Resolution ◆ Patterns depend on the scale ◼ Distribution ◆ Centrality and dispersion

Data Obiects a Data sets are made up of data objects a data object represents an entity ■上 xamples e sales database: customers store items sales medical database: patients, treatments university database: students, professors, courses Also called samples, examples, instances, data points, objects, tuples a Data objects are described by attributes a Database rows -> data objects columns ->attributes 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

5 Data Objects ◼ Data sets are made up of data objects. ◼ A data object represents an entity. ◼ Examples: ◆ sales database: customers, store items, sales ◆ medical database: patients, treatments ◆ university database: students, professors, courses ◼ Also called samples , examples, instances, data points, objects, tuples. ◼ Data objects are described by attributes. ◼ Database rows -> data objects; columns ->attributes

Attributes Attribute(or dimensions, features, variables a data field, representing a characteristic or feature of a data object E.g., customer D, name, address Types ◆ Nominal ◆ Binary ◆ Numeric: quantitative a Interval-scaled □ Ratio-sca|ed 6 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

6 Attributes ◼ Attribute (or dimensions, features, variables): a data field, representing a characteristic or feature of a data object. ◆ E.g., customer _ID, name, address ◼ Types: ◆ Nominal ◆ Binary ◆ Numeric: quantitative  Interval-scaled  Ratio-scaled

Attribute Types a Nominal: categories, states, or"names of things Hair color=auburn, black, blond, brown, grey, red, white) .marital status, occupation, ID numbers, zip codes Binary Nominal attribute with only 2 states(0 and 1) Symmetric binary: both outcomes equally important 口e.g., gender Asymmetric binary outcomes not equally important o e.g., medical test(positive Vs. negative o Convention: assign 1 to most important outcome( e.g., HIV positive) Ordinal Values have a meaningful order(ranking) but magnitude between successive values is not known Size =Small, medium, large), grades, army rankings 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

7 Attribute Types ◼ Nominal: categories, states, or “names of things” ◆ Hair_color = {auburn, black, blond, brown, grey, red, white} ◆ marital status, occupation, ID numbers, zip codes ◼ Binary ◆ Nominal attribute with only 2 states (0 and 1) ◆ Symmetric binary: both outcomes equally important  e.g., gender ◆ Asymmetric binary: outcomes not equally important.  e.g., medical test (positive vs. negative)  Convention: assign 1 to most important outcome (e.g., HIV positive) ◼ Ordinal ◆ Values have a meaningful order (ranking) but magnitude between successive values is not known. ◆ Size = {small, medium, large}, grades, army rankings

Numeric Attribute Types a Quantity(integer or real-valued ■ nterval a Measured on a scale of equal-sized units a Values have order > E.g., temperature in C or F, calendar dates 口 No true zero-point Ratio 口 Inherent zero- point a We can speak of values as being an order of magnitude larger than the unit of measurement (10 K is twice as high as 5K) >e.g., temperature in Kelvin, length, counts, monetary guantities 8 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

8 Numeric Attribute Types ◼ Quantity (integer or real-valued) ◼ Interval  Measured on a scale of equal-sized units  Values have order ➢ E.g., temperature in C˚or F˚, calendar dates  No true zero-point ◼ Ratio  Inherent zero-point  We can speak of values as being an order of magnitude larger than the unit of measurement (10 K˚ is twice as high as 5 K˚). ➢ e.g., temperature in Kelvin, length, counts, monetary quantities

Discrete vs. Continuous Attributes Discrete Attribute o Has only a finite or countably infinite set of values o E.g., 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 Continuous Attribute e Has real numbers as attribute values D E.g., temperature, height, or weight Practically, real values can only be measured and represented using a finite number of digits o Continuous attributes are typically represented as floating-point variables 同济大学软件学院 ool of Software Engineering. Tongpi Unversity

9 Discrete vs. Continuous Attributes ◼ Discrete Attribute ◆ Has only a finite or countably infinite set of values  E.g., 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 ◼ Continuous Attribute ◆ Has real numbers as attribute values  E.g., 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

Getting to Know your Data a Data objects and Attribute types a Basic Statistical Descriptions of data Data visualization Measuring Data Similarity and Dissimilarity a Summary 同济大学软件学院 10 ool of Software Engineering. Tongpi Unversity

10 Getting to Know Your Data ◼ Data Objects and Attribute Types ◼ Basic Statistical Descriptions of Data ◼ Data Visualization ◼ Measuring Data Similarity and Dissimilarity ◼ Summary