BA Quizzes 9 & 10
Which of the following is a disadvantage of neural networks?
All of the above are weaknesses of neural networks.
The choice of k (number of clusters in a Cluster Analysis) can be made using a variety of methods. Which of the following methods is appropriate in selecting the number of clusters
All of the methods listed above are appropriate
The choice of k (number of clusters in a Cluster Analysis) can be made using a variety of methods. Which of the following methods is appropriate in selecting the number of clusters.
All the methods listed above are appropriate
Neural Networks can be used to predict:
Both continuous and categorical dependent variables
The Euclidean distances of a customer from the different cluster centers are given below. Based on this information, which cluster the customer belongs to?
Cluster 4
Which of the following is an un-supervised data mining technique?
Cluster Analysis
Where would you most likely see a dendrogram?
In a hierarchical clustering algorithm
You want to group custommers in your dataset by similarity and assign labels to each group. What is the preferred analytic method to use for this task?
K-means clustering
What are the outputs generated by a k-Means clustering Analysis?
The centroids of the discovered cluster and the assignment of each input datum to a cluster.
Cluster analysis is a very attractive initial data-mining tool because it can be used to discover rules and patterns.
True
A good clustering technique will create low similarity within a cluster and high similarity between the clusters.
false
A required preparatory task in cluster analysis is to transform continuous variables into categorical variable types that can then be input into the model.
false
Cluster Analysis is a supervised learning technique.
false
In an online bookstore, making recommendations to customers concerning additional items to buy based on the buying patterns in prior transactions is an example of cluster Analysis technique.
false
In order to include a categorical variable in k-means cluster analysis in JMP, the data must be coded numerically. Therefore, categorical variables should be coded as 0/1.
false
Which of the following activation functions is not used in neural networks (in JMP)?
none of the above
A good clustering scheme will have little variation within clusters and signficant variation between clusters.
true
According to the text, the most popular choice for the number of hidden layers is one.
true
Cluster analysis is an un-supervised technique.
true
Cluster analysis refers to grouping of records that are similar to one another
true
In Cluster analysis, it is important to normalize the data to get rid of differences in scale.
true
In K-Mean clustering technique, the user has to pick the number of clusters.
true
K-Means clustering is well suited to the task of Market segmentation.
true
K-means algorithm is a typical algorithm for cluster analysis that uses "Euclidean distance" to find clusters.
true
Neural Networks were originally developed to understand biological neural networks.
true
Neural networks can be used for both continuous and categorical dependent (output) variables.
true
One advantage of neural networks is that there is very little chance of overfitting, so validation or testing data is not needed.
true
One disadvantage of neural networks is that they are slow learners.
true
One-way to decide on the number of clusters in a K-means cluster analysis is to arbitrarily pick a value.
true
The "K" in K-Means Cluster Analysis refers to the maximum number of variables that a clustering model can utilize.
true
The curse of dimensionality refers to the computational complexity of developing clusters using a large number of variables.
true
The most popular method for using model errors to update weights is called back propagation of error.
true
Training a neural network model involves estimating the weights that will lead to the best predictive results.
true
