Podcast
Questions and Answers
What is the purpose of data transformation routines in data mining?
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?
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?
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?
What technique is used to automatically generate concept hierarchies for the data?
What is the benefit of generating concept hierarchies for the data?
What is the benefit of generating concept hierarchies for the data?
What is the major task in data preprocessing that deals with combining data from multiple sources?
What is the major task in data preprocessing that deals with combining data from multiple sources?
What is the primary goal of data reduction?
What is the primary goal of data reduction?
What type of data is characterized by containing errors or outliers?
What type of data is characterized by containing errors or outliers?
What is the process of modifying the source data into different formats in terms of data types and values?
What is the process of modifying the source data into different formats in terms of data types and values?
What is the primary goal of data cleaning?
What is the primary goal of data cleaning?
What is an example of incomplete data?
What is an example of incomplete data?
What is the primary goal of data cleaning?
What is the primary goal of data cleaning?
What is the term used to describe the process of reducing the size of the dataset while maintaining its integrity?
What is the term used to describe the process of reducing the size of the dataset while maintaining its integrity?
What is the benefit of stratified sampling in data preparation?
What is the benefit of stratified sampling in data preparation?
What is the primary goal of data integration?
What is the primary goal of data integration?
What is the purpose of data transformation in data preparation?
What is the purpose of data transformation in data preparation?
What is the term used to describe the degree to which the data is trusted by users?
What is the term used to describe the degree to which the data is trusted by users?
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
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
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.
