17 Questions
What is the purpose of data transformation routines in data mining?
To convert the data into appropriate forms for mining
What is the outcome of normalization in data transformation?
Attribute data are scaled to fall within a small range such as 0.0 to 1.0
What is the purpose of data discretization in data transformation?
To transform numeric data by mapping values to interval or concept labels
What technique is used to automatically generate concept hierarchies for the data?
Data discretization
What is the benefit of generating concept hierarchies for the data?
It allows for mining at multiple levels of granularity
What is the major task in data preprocessing that deals with combining data from multiple sources?
Data Integration
What is the primary goal of data reduction?
To obtain a reduced representation of the dataset that produces the same analytical results
What type of data is characterized by containing errors or outliers?
Noisy data
What is the process of modifying the source data into different formats in terms of data types and values?
Data Transformation
What is the primary goal of data cleaning?
To remove errors and inconsistencies from the dataset
What is an example of incomplete data?
Occupation = ''
What is the primary goal of data cleaning?
To fill in missing values, smooth noisy data, and resolve inconsistencies
What is the term used to describe the process of reducing the size of the dataset while maintaining its integrity?
Data Reduction
What is the benefit of stratified sampling in data preparation?
It ensures that the sample is representative of the population
What is the primary goal of data integration?
To combine data from multiple sources
What is the purpose of data transformation in data preparation?
To convert the data into a suitable format
What is the term used to describe the degree to which the data is trusted by users?
Believability
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
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.
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