Data Principles week 8 part 3

What is binning in the context of discretisation?

• A) A supervised method of splitting data into a specified number of bins

• B) A top-down unsupervised splitting technique based on a specified number of bins

• C) A bottom-up supervised method of combining data into a single bin

• D) A method of visualizing data without splitting it into bins

What is the difference between equi-width binning and equi-depth binning?

• A) Equi-width binning divides the range of data into intervals of equal size, while equi-depth binning divides the data into intervals with an equal number of points

• B) Equi-width binning divides the data into intervals with an equal number of points, while equi-depth binning divides the range of data into intervals of equal size

• C) Equi-width binning is a supervised method, while equi-depth binning is unsupervised

• D) Equi-width binning is used for categorical data, while equi-depth binning is used for numerical data

What is a histogram used for in data analysis?

• A) To calculate the mean and median of a dataset

• B) To represent the frequency distribution of categorical data

• C) To partition the values of an attribute into disjoint ranges called buckets or bins

• D) To find the correlation between two variables

What is the approach of a clustering algorithm when applied to discretize a numerical attribute?

• Supervised, bottom-up merge

• Supervised, top-down split

• Unsupervised, bottom-up merge

• Unsupervised, top-down split or bottom-up merge

What is the result of applying a clustering algorithm to partition the values of an attribute?

• The values are converted into a single cluster.

• The values are organized into a linear sequence.

• The values are partitioned into clusters based on similarity.

• The values are distributed randomly.

Under what condition is a clustering algorithm effective for discretization?

• When data is clustered and smeared

• When data is clustered but not if data is smeared

• When data is not clustered and not smeared

• When data is uniformly distributed

Which specific cluster analysis method is mentioned in the learning material?

• Cluster analysis using hierarchical clustering

• Cluster analysis using k-means

• Cluster analysis using DBSCAN

• Cluster analysis using spectral clustering

What approach is used in decision tree analysis for discretisation?

• Bottom-up splitting

• Binary splitting

• Top-down splitting

• Random splitting

In the context of decision tree analysis, what does 'supervised' refer to?

• Using a predefined split point without class labels

• Ignoring class labels in the splitting process

• Making use of the class label for splitting

• Using an unsupervised algorithm to determine the split point

What is used to determine the split point in decision tree analysis?

• Gini index

• Chi-square

• Entropy

• Variance reduction