## Data Mining Quiz 4 Answers

Sufficient Number of output nodes required in an ANN used for two-class classification problem is:

Answer: 1

How are the weights and biases initialized in an ANN in general?

Can be initialized randomly

In which neural network, the links may connect nodes within the same layer or nodes from one layer to the previous layers?

Recurrent neural network

Neural Networks are complex ______________ with many parameters.

Nonlinear Functions

Artificial neural network used for:

a. Pattern Recognition b. Classification c. Clustering

A neuron with 3 inputs has the weight vector [0.2 -0.1 0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the total input to the neuron is:

0.02

Under which of the following situation would you expect overfitting to happen?

With training iterations error on training set decreases but test set increases

Which of the following statement is NOT true about clustering? a) It is a supervised learning technique b) It is an unsupervised learning technique

a) It is a supervised learning technique

Which clustering technique start with the points as individual clusters and, at each step, merge the closest pair of clusters

Agglomerative clustering

DBSCAN is a___________ algorithm

Partitional clustering

The leaves of a dendogram in hierarchical clustering represent?

Individual data points

Distance between two clusters in complete linkage clustering is defined as:

Distance between the furthest pair of points between the clusters

Consider a set of five 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), and p5=(0, 0.5). Euclide-an distance is the distance function. Single linkage clustering is used to cluster the points into two clusters. The clusters are:

{p1, p4, p5} {p2, p3}

Consider a set of five 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), and p5=(0, 0.5). Euclide-an distance is the distance function. Complete linkage clustering is used to cluster the points into two clus-ters. The clusters are:

{p1, p4, p5} {p2, p3}

Consider a set of five 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), and p5=(0, 0.5). Euclidean distance is the distance function. The k-means algorithm is used to cluster the points into two clusters. The initial cluster centers are p1 and p5. The clusters after two iterations of k-means are:

{p1, p4, p5} {p2, p3}

Given a set of seven 2-dimensional points p1=(0, 0), p2=(5, 0), p3=(5, 1), p4=(0, 1), p5=(0, 0.5), p6=(0, 9), and p7=(5.5, 1). Euclidean distance is the distance function. The DBSCAN algorithm is used to cluster the points. Epsilon = 1, and MinPts = 2 is used for DBSCAN. The clusters and outliers obtained are:

Clusters: {p1, p4, p5} {p2, p3, p7}; Outlier: p6

Target variable in regression is continuous or discrete?

Continuous

Regression is used in:

Predictive data mining

Regression finds out the model parameters which produces the least square error between

Output value and Target value

A time series prediction problem is often solved using?

Autoregression

In principal component analysis, the projected lower dimensional space corresponds to

Eigenvectors of the data covariance matrix

Total Number of Questions: 21

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