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What is another term for attribute in the context of data mining?
What is another term for attribute in the context of data mining?
- Indicator
- Variable (correct)
- Parameter
- Constant
In data mining, what is a collection of attributes that describe an object known as?
In data mining, what is a collection of attributes that describe an object known as?
- Node
- Record (correct)
- Element
- Factor
What are attribute values in data mining?
What are attribute values in data mining?
- Probabilities assigned to an attribute
- Categories assigned to an attribute
- Numbers or symbols assigned to an attribute (correct)
- Weights assigned to an attribute
What is the distinction between attributes and attribute values in data mining?
What is the distinction between attributes and attribute values in data mining?
In data mining, what is an example of different attributes being mapped to the same set of values?
In data mining, what is an example of different attributes being mapped to the same set of values?
What is the term used in data mining for the way an attribute is measured not matching the attribute's properties?
What is the term used in data mining for the way an attribute is measured not matching the attribute's properties?
What is another term for object in the context of data mining?
What is another term for object in the context of data mining?
In data mining, what is a property or characteristic of an object known as?
In data mining, what is a property or characteristic of an object known as?
Which type of attribute captures only the order properties of length?
Which type of attribute captures only the order properties of length?
What type of attribute preserves both order and additivity properties of length?
What type of attribute preserves both order and additivity properties of length?
Which attribute type encompasses the notion of 'good, better, best'?
Which attribute type encompasses the notion of 'good, better, best'?
What type of attribute has real numbers as attribute values?
What type of attribute has real numbers as attribute values?
Which attribute type can be described in terms of transformations that do not change the meaning of the attribute?
Which attribute type can be described in terms of transformations that do not change the meaning of the attribute?
What type of attribute has only a finite or countably infinite set of values?
What type of attribute has only a finite or countably infinite set of values?
Which type of attribute is represented as floating-point variables?
Which type of attribute is represented as floating-point variables?
What type of attribute is regarded as important only in its presence (non-zero attribute value)?
What type of attribute is regarded as important only in its presence (non-zero attribute value)?
Which type of attribute involves operations like addition and multiplication?
Which type of attribute involves operations like addition and multiplication?
What type of attribute transformation involves any permutation of values?
What type of attribute transformation involves any permutation of values?
Which type of attribute involves an order-preserving change of values?
Which type of attribute involves an order-preserving change of values?
What type of attribute is a special case of discrete attributes and often represented as integer variables?
What type of attribute is a special case of discrete attributes and often represented as integer variables?
What is the purpose of aggregation in data preprocessing?
What is the purpose of aggregation in data preprocessing?
Which type of sampling allows the same object to be picked up more than once?
Which type of sampling allows the same object to be picked up more than once?
What is the key principle for effective sampling?
What is the key principle for effective sampling?
What is the purpose of dimensionality reduction in data mining?
What is the purpose of dimensionality reduction in data mining?
Which technique is used for dimensionality reduction and aims to capture the maximum amount of variation in the data?
Which technique is used for dimensionality reduction and aims to capture the maximum amount of variation in the data?
What issue arises when merging data from heterogeneous sources?
What issue arises when merging data from heterogeneous sources?
What is the purpose of data cleaning in data preprocessing?
What is the purpose of data cleaning in data preprocessing?
What does the curse of dimensionality refer to?
What does the curse of dimensionality refer to?
What is the main purpose of sampling in data mining?
What is the main purpose of sampling in data mining?
What is the purpose of attribute transformation in data preprocessing?
What is the purpose of attribute transformation in data preprocessing?
Which type of sampling ensures an equal probability of selecting any particular item?
Which type of sampling ensures an equal probability of selecting any particular item?
What does aggregation aim to achieve in data preprocessing?
What does aggregation aim to achieve in data preprocessing?
What are some important characteristics of data according to the text?
What are some important characteristics of data according to the text?
What type of data involves a set of items in each record?
What type of data involves a set of items in each record?
What does noise refer to in the context of data quality problems?
What does noise refer to in the context of data quality problems?
Which type of data represents data objects as points in a multi-dimensional space?
Which type of data represents data objects as points in a multi-dimensional space?
What type of data includes sequences of transactions, genomic sequence data, and spatio-temporal data?
What type of data includes sequences of transactions, genomic sequence data, and spatio-temporal data?
What is an example of graph data according to the text?
What is an example of graph data according to the text?
What type of data quality problem refers to considerably different data objects?
What type of data quality problem refers to considerably different data objects?
What type of data consists of a collection of records with fixed attributes?
What type of data consists of a collection of records with fixed attributes?
What type of data is represented as term vectors with term frequency values?
What type of data is represented as term vectors with term frequency values?
What type of data involves a modification of original values in the context of data quality problems?
What type of data involves a modification of original values in the context of data quality problems?
Which type of data quality problem can be handled by elimination or estimation?
Which type of data quality problem can be handled by elimination or estimation?
What type of data involves generic graphs, molecules, and webpages?
What type of data involves generic graphs, molecules, and webpages?
Which technique aims to reduce redundant and irrelevant features in the dataset?
Which technique aims to reduce redundant and irrelevant features in the dataset?
What does feature creation involve?
What does feature creation involve?
Which technique involves mapping data to a new space through Fourier transform and wavelet transform?
Which technique involves mapping data to a new space through Fourier transform and wavelet transform?
What does discretization involve?
What does discretization involve?
Which method is commonly used in classification and involves unsupervised and supervised approaches?
Which method is commonly used in classification and involves unsupervised and supervised approaches?
What does binarization involve?
What does binarization involve?
What does attribute transformation involve?
What does attribute transformation involve?
What is normalization?
What is normalization?
What is the goal of attribute transformation?
What is the goal of attribute transformation?
What are dimensionality reduction techniques crucial for?
What are dimensionality reduction techniques crucial for?
What is the Iris Plant data set used for?
What is the Iris Plant data set used for?
Which discretization methods include equal interval width, equal frequency, and K-means approaches?
Which discretization methods include equal interval width, equal frequency, and K-means approaches?
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Study Notes
Introduction to Data Mining: Dimensionality Reduction Techniques
- Dimensionality reduction includes techniques such as feature subset selection, feature creation, and attribute transformation.
- Feature subset selection aims to reduce redundant and irrelevant features in the dataset.
- Feature creation involves creating new attributes that capture important information more efficiently than the original attributes.
- Mapping data to a new space can be achieved through techniques like Fourier transform and wavelet transform.
- Discretization involves converting continuous attributes into ordinal attributes, commonly used in classification.
- The Iris Plant data set, obtained from the UCI Machine Learning Repository, is used as an example to illustrate discretization.
- Discretization methods include unsupervised and supervised approaches, as well as equal interval width, equal frequency, and K-means approaches.
- Binarization maps continuous or categorical attributes into one or more binary variables, often used for association analysis.
- Attribute transformation involves mapping the entire set of values of an attribute to a new set of replacement values using functions such as xk, log(x), ex, and |x|.
- Normalization is a type of attribute transformation that adjusts differences among attributes in terms of frequency of occurrence, mean, variance, and range.
- The goal of attribute transformation is to remove unwanted, common signals and adjust for differences among attributes.
- These dimensionality reduction techniques are crucial for improving the efficiency and effectiveness of data mining tasks.
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