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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. - ANSWER--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. - ANSWER--
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 - ANSWER--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 - ANSWER--Matching coefficient
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. - ANSWER-Euclidian
Distance
It requires replacing the raw value of data with its z-score. - ANSWER-Euclidian
Distance
The lower the measure the better - ANSWER-Euclidian Distance
,It is for categorical Data - ANSWER-Matching Coefficient
It is a ratio of number of columns with matching categorical values to the total
number of categorical columns. - ANSWER-Matching Coefficient
Which of the following is a business application of cluster analysis?
-Affinity analysis
-Outlier detection
-Market basket analysis
-Sentiment analysis - ANSWER--Outlier detection
Match the situation on the left with the clustering method on the right.
-You have 300 rows of data. - ANSWER-Hierarchical Clustering
You only have numerical data to work with. - ANSWER-K-Means Clustering
You have outliers in the data. - ANSWER-K-Means Clustering
You want to experiment with different ways to calculate the distance between
clusters. - ANSWER-Hierarchical Clustering
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. - ANSWER--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.
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.
-Reassigns each object to a cluster based on the cluster means in each
iteration.
-Single linkage is the default distance measure used in JMP Pro
-Ward's minimum variance is the default distance measure used in JMP Pro.
-Two most similar clusters are combined into one in each iteration. - ANSWER--
Each object is placed in its own cluster when started.
-All objects are placed in one cluster when stopped.
-Ward's minimum variance is the default distance measure used in JMP Pro.
-Two most similar clusters are combined into one in each iteration.
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