AN 300, EXAM 2 WITH QUESTIONS AND 100% VERIFIED ANSWERS. // ALREADY A+ GRADED. // 2024/2025/2026 LATEST UPDATE
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AN 300,
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AN 300,
AN 300, EXAM 2 WITH QUESTIONS AND 100% VERIFIED
ANSWERS. // ALREADY A+ GRADED. //
2024/2025/2026 LATEST UPDATE
Which of the following is true about CCC? Check all that apply.
-A value greater than two is highly desirable.
-It is used to tell the goodness of fit of clustering.
-It stands fo...
AN 300, EXAM 2 WITH QUESTIONS AND 100% VERIFIED
ANSWERS. // ALREADY A+ GRADED. //
2024/2025/2026 LATEST UPDATE
Which of the following is true about CCC? Check all that apply.
-A value greater than two is highly desirable.
-It is used to tell the goodness of fit of clustering.
-It stands for Cubic Clustering Criterion.
-It is related to the proportion of variance in the data accounted for by the
clusters.
-It is related to the proportion of matching values in a column against all
columns.
-A negative value is impossible.
-It stands for Complete Clustering Criterion. - Ans✔✔--A value greater than two
is highly desirable.
-It is used to tell the goodness of fit of clustering.
-It stands for Cubic Clustering Criterion.
-It is related to the proportion of variance in the data accounted for by the
clusters.
Match the description on the left with the measure to use on the right.
-It is the length of a straight line between two objects. - Ans✔✔-Euclidian
Distance
It requires replacing the raw value of data with its z-score. - Ans✔✔-Euclidian
Distance
The lower the measure the better - Ans✔✔-Euclidian Distance
It is for categorical Data - Ans✔✔-Matching Coefficient
,It is a ratio of number of columns with matching categorical values to the total
number of categorical columns. - Ans✔✔-Matching Coefficient
Which of the following is a business application of cluster analysis?
-Affinity analysis
-Outlier detection
-Market basket analysis
-Sentiment analysis - Ans✔✔--Outlier detection
Match the situation on the left with the clustering method on the right.
-You have 300 rows of data. - Ans✔✔-Hierarchical Clustering
You only have numerical data to work with. - Ans✔✔-K-Means Clustering
You have outliers in the data. - Ans✔✔-K-Means Clustering
You want to experiment with different ways to calculate the distance between
clusters. - Ans✔✔-Hierarchical Clustering
Which of the following is true about cluster analysis? Check all that apply.
-It is a descriptive analytics technique
-It is to discover associations between objects.
-It is used to discover natural groupings of objects.
-It is to answer what has happened questions.
-It is a descriptive analytics method.
-It is to answer what could happen questions. - Ans✔✔--It is a descriptive
analytics technique.
-It is used to discover natural groupings of objects.
-It is to answer what has happened questions.
Which of the following is a characteristic of a cluster analysis problem? (Check all
that apply):
, -The data that describes the object must be given.
-Its objective is to maximize similarities of objects between groups.
-The data on group memberships must be given.
-It is about how to discover
-It is about how to organize objects into groups.
-Its objective is to maximize similarities of objects within groups. - Ans✔✔--The
data that describes the object must be given.
-It is about how to organize objects into groups.
-Its objective is to maximize similarities of objects within groups.
You have data on the weight and height of patients. Which similarity measure
should be used to calculate how similar a group of patients is to one another?
-Manhattan coefficient
-Correlation coefficient
-Euclidean distance
-Matching coefficient
-Straight line distance - Ans✔✔--Euclidean distance
You have data on the gender and income levels of customers. Which similarity
measure should be used to calculate how similar a group of customers is to one
another?
-Correlation coefficient
-Matching coefficient
-Straight line distance
-Manhattan coefficient
-Euclidean distance - Ans✔✔--Matching coefficient
Which of the following is TRUE about the Hierarchical Clustering process? Check
all that apply.
-Each object is assigned to one of the k clusters based on a seed point.
-Clusters are stabilized when stopped.
-Each object is placed in its own cluster when started.
-All objects are placed in one cluster when stopped.
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