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K-Means Clustering: Fundamentals and Applications, Exams of Data Mining

Alexandria UniversityData Mining

A comprehensive overview of the k-means clustering algorithm, a widely used unsupervised learning technique for grouping data objects into multiple clusters based on their similarity. It covers the key concepts of clustering, the k-means algorithm, and its practical applications. The characteristics of k-means, such as the number of clusters, the distance metric, and the iterative process of cluster centroids optimization. It also includes examples and exercises to illustrate the algorithm's implementation and interpretation of results. This resource would be valuable for students and researchers interested in understanding the fundamentals of clustering and its applications in data analysis, pattern recognition, and decision-making.

Typology: Exams

2020/2021

Uploaded on 11/25/2022

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shehab-eldin-said 🇪🇬

6 documents

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Sheet Section 6
1. ……. Is the process of grouping a set of data objects into multiple groups
a. Classification
b. Regression
c. Clustering
d. Algorithm
Answer: c
2. Clustering is a prediction model
a. True
b. False
Answer: b
3. Clustering is
a. Supervised learning
b. Semi supervised learning
c. Unsupervised learning
d. Reinforcement learning
Answer: c
4. In K-means, K stands for number of
a. objects
b. datasets
c. clusters
d. error functions
Answer: c
5. A cluster is a group of data that shares similar features with objects in other
groups
a. True
b. False
Answer: b
Page 1
pf3
pf4
pf5

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Download K-Means Clustering: Fundamentals and Applications and more Exams Data Mining in PDF only on Docsity!

Sheet Section 6

  1. ……. Is the process of grouping a set of data objects into multiple groups a. Classification b. Regression c. Clustering d. Algorithm Answer: c
  2. Clustering is a prediction model a. True b. False Answer: b
  3. Clustering is a. Supervised learning b. Semi supervised learning c. Unsupervised learning d. Reinforcement learning Answer: c
  4. In K-means, K stands for number of a. objects b. datasets c. clusters d. error functions Answer: c
  5. A cluster is a group of data that shares similar features with objects in other groups a. True b. False Answer: b
  1. Assume, you want to cluster 7 observations into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has the following observations:C1:{(2,2),(4,4),(6,6)}, C2:{(0,4),(4,0)}, C3:{(5,5),(9,9)} What will be the cluster centroids if you want to proceed for second iteration? a. C1: (4,4), C2: (2,2), C3: (7,7) b. C1: (6,6), C2: (4,4), C3: (9,9) c. C1: (2,2), C2: (0,0), C3: (5,5) d. None of the above Answer: a
  2. Which statement is true about the K-Means algorithm? a. All attribute values must be categorical. b. The output attribute must be categorical. c. Attribute values may be either categorical or numeric. d. All attributes must be numeric. Answer: d
  3. If I want to group items by similarity I can use a. Clustering b. Association Rules c. Regression d. Classification Answer: a
  4. In K-Means there must be a distance metric defined over the variable space. a. True b. False Answer: a
  1. Assume, you want to cluster 7 observations into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has the following observations:C1:{(2,2),(4,4),(6,6)}, C2:{(0,4),(4,0)}, C3:{(5,5),(9,9)} What will be the cluster centroids if you want to proceed for second iteration? Answer: C1: ( 2 + 4 + 6 3 , 2 + 4 + 6 3

¿=¿(4, 4), C2: (

0 + 4 2 , 4 + 0

=¿(2, 2), C3:

5 + 9 2 , 5 + 9

  1. Use the next table to divide the set into 2 cluster using K-means clustering (perform two iterations only).

Answer:

Step 1 :

Initialization: Randomly we choose following two centroids (k=2) for

two clusters.

In this case the 2 centroid are: m1= (1.0,1.0) and m2= (5.0,7.0).

Individual Variable 1 Variable 2

Individual Mean Vector

Individual Variable 1 Variable 2 M1 1.0 1. M2 1.5 2. M3 3.0 4. M4 5.0 7. M5 3.5 5. M6 4.5 5. M7 3.5 4.

Group 1 1 (1.0,1.0)

Group 2 4 (5.0,7.0)

Step 2:

 Thus, we obtain two clusters containing: {1, 2, 3} and

Individual Cluster 1 Cluster 2

 Their new centroids are:

m 1 =(

1 3 (1.0+ 1.5+3.0) , 1 3

m 2 =(

1 4 (5.0+ 3.5+4.5+3.5 ) , 1 4