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.

Attribute (or dimensions, features, variables): a data field, representing a characteristic or feature of a data object.
E.g., customer _ID, name, address

Attribute  Types:

• Nominal
• Binary
• Numeric: quantitative
  • Interval-scaled
  • Ratio-scaled

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
• 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

• 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

 

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