Do you know everything about data mining? You can take up this data mining trivia quiz to check your knowledge on the same. Living in a world run and managed using soft technology, data has become an essential part of human existence. The quantity of data generated and stored every day makes it hard to retrieve when needed. How much do you know about data mining? The quiz results will show how well you know about this topic. All the best!
Dictionary Hack Approach
Dynamic brute force approach
Brute force approach
Embedded approach
Filter approach
Wrapper approaches:
World Wide Web, Molecular Structures
Spatial Data, Temporal Data, Sequential Data, Genetic Sequence Data
Generic Data, Inferential Data, Continuous Data
Data Matrix, Document Data, Transaction Data
World Wide Web, Molecular Structures
Spatial Data, Temporal Data, Sequential Data, Genetic Sequence Data
Generic Data, Inferential Data, Continuous Data
Data Matrix, Document Data, Transaction Data
Generality
Dimensionality
Resolution
Spacial
Sparsity
Aggregation
Distortion
Sequel Ordering
Sampling
Dimensionality Reduction
Bias Resolution
Feature subset selection
Feature creation
Rendering Objects
Discretization and Binarization
Pixelation
Attribute Transformation
Attribute Regression
Brute Force approach
Feature Extraction
Mapping Data to New Space
Sparsity Feature
Feature Construction
It is the main technique employed for data selection
Statisticians sample because obtaining the entire set of data of interest is too expensive or time-consuming.
It is used in data mining because processing the entire set of data or interests is too expensive or time-consuming.
Because it is easier and viable to use
Distinctness & order
Distinctness, order & addition
Distinctness
All of the above
Distinctness & order
Distinctness, order & addition
Distinctness
All of the above
If data objects have the same fixed set of numeric attributes, then the data objects can be thought of as points in a multi-dimensional space, where each dimension represents a distinct attribute.
Such data set can be represented by an m by n matrix, where there are m rows, one for each object, and n columns, one for each attribute.
Neither A nor B
Both A and B
True
False
Distinctness & order
Distinctness, order & addition
Distinctness
All 4 properties
True
False
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