What is Data? Also called samples, examples, instances, data points, objects, tuples.

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Hugh Maxwell
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1 What is Data? Data sets are made up of data objects. A data object represents an entity. Also called samples, examples, instances, data points, objects, tuples. Data objects are described by 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 Data Mining 1

2 10 A Data Object Attributes Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No Cheat Database rows -> data objects; columns -> attributes. Objects 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 2

3 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 Mining 3

4 Attribute Types There are different types of attributes 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) Ordinal Values have a meaningful order (ranking) but magnitude between successive values is not known. Size = {small, medium, large}, grades, army rankings Data Mining 4

5 Attribute Types: 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 Data Mining 5

6 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 Data Mining 6

7 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: Data Mining 7

8 Important Characteristics of Structured Data Dimensionality Curse of dimensionality Sparsity Only presence counts Resolution Patterns depend on the scale Distribution Centrality and dispersion Data Mining 8

9 10 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 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 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 9

10 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 Projection of x Load Projection of y load Distance Load Thickness Data Mining 10

11 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 Data Mining 11

12 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 5 Coke, Diaper, Milk Data Mining 12

13 Graph Data Examples: Generic graph and HTML Links <a href="papers/papers.html#bbbb"> Data Mining </a> <li> <a href="papers/papers.html#aaaa"> Graph Partitioning </a> <li> <a href="papers/papers.html#aaaa"> Parallel Solution of Sparse Linear System of Equations </a> <li> <a href="papers/papers.html#ffff"> N-Body Computation and Dense Linear System Solvers 5 Data Mining 13

14 Chemical Data Benzene Molecule: C 6 H 6 Data Mining 14

15 Sequences of transactions Items/Events Ordered Data An element of the sequence Data Mining 15

16 Ordered Data Genomic sequence data GGTTCCGCCTTCAGCCCCGCGCC CGCAGGGCCCGCCCCGCGCCGTC GAGAAGGGCCCGCCTGGCGGGCG GGGGGAGGCGGGGCCGCCCGAGC CCAACCGAGTCCGACCAGGTGCC CCCTCTGCTCGGCCTAGACCTGA GCTCATTAGGCGGCAGCGGACAG GCCAAGTAGAACACGCGAAGCGC TGGGCTGCCTGCTGCGACCAGGG Data Mining 16

Lecture 6 - Data Mining Processes

Lecture 6 - Data Mining Processes Dr. Songsri Tangsripairoj Dr.Benjarath Pupacdi Faculty of ICT, Mahidol University 1 Cross-Industry Standard Process for Data Mining (CRISP-DM) Example Application: Telephone