Data Mining Concepts

Data Characteristics

• Data is a collection of data objects and their attributes.
• Attributes are also known as variables, fields, characteristics, or features.
• A data object is a collection of attributes that describe an object, also known as a record, point, case, sample, entity, or instance.

Types of Attributes

• Nominal data: qualitative, cannot be measured or compared with numbers, and represents a category with no inherent order or hierarchy. Examples: gender, race, religion, and occupation.
• Ordinal data: qualitative, can be ranked in a particular order, but the distance between values is not uniform. Examples: education level, social status.
• Binary data: has only two possible values, often represented as 0 or 1. Examples: yes/no, true/false, pass/fail.
• Symmetric attribute: both values or states are considered equally important or interchangeable. Example: gender (male and female).
• Asymmetric attribute: the two values or states are not equally important or interchangeable. Example: result (pass and fail).
• Interval data: quantitative, with equal intervals between consecutive values, but no absolute zero point. Examples: temperature, IQ scores, time.
• Ratio data: quantitative, with an absolute zero point, and ratios can be computed. Examples: height, weight, income.
• Text data: unstructured data in the form of text, used in sentiment analysis, text classification, and topic modeling tasks.

Database Management

• Transactional database: a database management system that can undo a database transaction if it is not performed appropriately.

Data Mining

• Data mining enables organizations to obtain knowledge-based data, make lucrative modifications, and make decisions.
• Advantages of data mining:
  • Cost-efficient
  • Facilitates automated discovery of hidden patterns and prediction of trends and behaviors
  • Can be induced in new and existing systems
  • Quick process for analyzing large amounts of data
• Disadvantages of data mining:
  • Organizations may sell customer data to other organizations
  • Data mining analytics software can be difficult to operate and requires advanced training
  • Different algorithms used in data mining tools can lead to selection challenges
  • Data mining techniques are not always precise and can lead to severe consequences in certain conditions

This quiz covers essential concepts in data mining, including patterns, data objects, attributes, and data sets. Test your understanding of these fundamental ideas!