Data Objects and Attributes Overview

Data Objects

• Data sets are comprised of data objects, which represent entities.
• Examples of entities include customers, products, and sales in a sales database; patients and treatments in a medical database; and students, professors, and courses in a university database.
• Data objects are also called objects, data points, examples, instances, records, samples, or tuples.
• Attributes describe data objects.

Attributes

• An attribute is a data field that describes a characteristic or feature of a data object.
• Examples of attributes are age, name, gender, and address.
• The term dimension is often used in data warehousing, while machine learning literature uses feature and statisticians use variable.
• The term attribute is commonly used by data mining and database professionals.
• Observed values for a given attribute are known as observations.
• An attribute/feature vector is a set of attributes that are used to describe a given object.

Attribute Types

• Attributes can be classified into two main types: quantitative and qualitative.
• Quantitative attributes are numeric values (real or integer).
• Qualitative attributes are categorical values.

Categorical Attribute Types

• Nominal: Categories without ordering.
  • Examples: color (black, blond, brown, grey, red, white) and marital status (single, married, divorced, widowed)
• Binary: Attributes with two states, 0 and 1, such as gender (male, female).
  • Symmetric binary: Both outcomes are equally important.
  • Asymmetric binary: Outcomes have different importance, such as medical test results (positive, negative).
• Ordinal: Values with order but not necessarily equal intervals, such as sizes (small, medium, large) and grades (A+, A, B+, B, C+, C, D, F).

Numeric Attribute Types

• Interval: Values are measured on a scale with equal units but lack a true zero point.
  • Examples: temperature, calendar dates, intelligence scores, and blood pressure.
• Ratio: Values have a true zero point, allowing for comparisons of magnitudes.
  • Examples: length, counts, monetary quantities, and temperature in Kelvin.