Data Transformation in Data Mining: Discretization and Concept Hierarchy Generation

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Questions and Answers

What is the purpose of data transformation routines in data mining?

To convert the data into appropriate forms for mining (correct)

To visualize the data

To analyze the data using statistical methods

To reduce the data size

What is the outcome of normalization in data transformation?

Attribute data are scaled to fall within a large range

Attribute data are scaled to fall within a small range such as 0.0 to 1.0 (correct)

Concept hierarchies are generated for the data

Nominal data are converted to numeric data

What is the purpose of data discretization in data transformation?

To transform numeric data by mapping values to interval or concept labels (correct)

To analyze the data using statistical methods

To generate concept hierarchies for the data

To convert nominal data to numeric data

What technique is used to automatically generate concept hierarchies for the data?

Data discretization Signup and view all the answers

What is the benefit of generating concept hierarchies for the data?

It allows for mining at multiple levels of granularity Signup and view all the answers

What is the major task in data preprocessing that deals with combining data from multiple sources?

Data Integration Signup and view all the answers

What is the primary goal of data reduction?

To obtain a reduced representation of the dataset that produces the same analytical results Signup and view all the answers

What type of data is characterized by containing errors or outliers?

Noisy data Signup and view all the answers

What is the process of modifying the source data into different formats in terms of data types and values?

Data Transformation Signup and view all the answers

What is the primary goal of data cleaning?

To remove errors and inconsistencies from the dataset Signup and view all the answers

What is an example of incomplete data?

Occupation = '' Signup and view all the answers

What is the primary goal of data cleaning?

To fill in missing values, smooth noisy data, and resolve inconsistencies Signup and view all the answers

What is the term used to describe the process of reducing the size of the dataset while maintaining its integrity?

Data Reduction Signup and view all the answers

What is the benefit of stratified sampling in data preparation?

It ensures that the sample is representative of the population Signup and view all the answers

What is the primary goal of data integration?

To combine data from multiple sources Signup and view all the answers

What is the purpose of data transformation in data preparation?

To convert the data into a suitable format Signup and view all the answers

What is the term used to describe the degree to which the data is trusted by users?

Believability Signup and view all the answers

Study Notes

Data Transformation

Data transformation routines convert data into suitable forms for mining
Normalization scales attribute data to fall within a small range (e.g., 0.0 to 1.0)
Other examples include data discretization and concept hierarchy generation

Data Discretization

Transforms numeric data by mapping values to interval or concept labels
Techniques used: binning, histogram analysis, cluster analysis, decision tree analysis, and correlation analysis
Automatically generates concept hierarchies for data, allowing for mining at multiple levels of granularity

Data Preprocessing

Refers to the process of converting source data into a format suitable for mining
Major tasks include:
- Data Cleaning: handling incomplete, noisy, and inconsistent data
- Data Integration: combining data from multiple sources to reduce redundancies and inconsistencies
- Data Reduction: obtaining a reduced representation of the dataset
- Data Transformation: modifying data formats and values

Data Quality

Factors that comprise data quality:
- Accuracy: represents reality
- Completeness: availability of necessary data
- Consistency: equality within and between datasets
- Timeliness: availability of data when needed
- Believability: trusted by users
- Interpretability: ease of understanding

Data Cleaning

Deals with real-world data issues:
- Incomplete Data: missing attribute values or lacking certain attributes
- Noisy Data: containing errors or outliers
- Inconsistent Data: containing discrepancies in codes or names

Data Preparation

Also referred to as Data Wrangling or Data Munging
Importance: data have quality if they satisfy the requirements of the intended use

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Description

Learn about data transformation techniques in data mining, including normalization, data discretization, and concept hierarchy generation. Understand how these methods prepare data for mining and enable analysis at multiple levels of granularity.