Machine Learning – II JUNE 2024

Description

Machine Learning – II

June 2024 Examination

 

 

1. Use the distance matrix in Table1 to perform single link hierarchical clustering. Show your results by drawing a dendrogram. The dendrogram should clearly show the order in which the points are merged. (10 Marks)

Table 1 Distance matrix       

	Pl	P2	P3	P4	PS
Pl	0.00	0.10	0.41	o.ss	0.3S
P2	0.10	0.00	0.64	0.47	0.98
P3	0.41	0.64	0.00	0.44	0.8S
P4	o.ss	0.47	0.44	0.00	0.76
PS	0.3S	0.98	0.8S	0.76	0.00

                                                           

Ans 1.

Introduction

Hierarchical clustering is an essential technique in the realm of machine learning and data analysis, primarily used for grouping similar objects into clusters. This method does not require the number of clusters to be specified in advance, which makes it particularly advantageous for exploratory data analysis. Hierarchical clustering can be performed using different linkage criteria, such as single linkage, complete linkage, and average linkage, each defining the distance between clusters differently. In single linkage hierarchical clustering, also known as the nearest neighbor method, the distance between two clusters is defined as the shortest distance between points in the two clusters. This approach tends to produce elongated and less compact clusters, making it suitable for identifying clusters with irregular shapes. The result of It is only half solved

 

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2. A database has five transactions. Apply apriori with minimum support of 30% and minimum confidence of 75%. (10 Marks)

Transaction ID	Items
1	1, 3, 4, 6
2	2, 3, 5, 7
3	1, 2, 3, 5, 8
4	2, 5, 9, 10
5	1, 10

 

Ans 2.

Introduction

In the realm of data mining, the Apriori algorithm stands as a cornerstone for discovering frequent itemsets and generating association rules within transactional databases. This algorithm plays a crucial role in market basket analysis, helping businesses understand customer purchasing behaviors, identify patterns, and make informed decisions about inventory management, cross-selling, and promotional strategies. The Apriori algorithm operates by iteratively identifying frequent itemsets, those which appear together in transactions more often than a specified threshold, and then deriving strong association rules from these itemsets based on a minimum confidence level. In this context, we explore the application of the Apriori algorithm on a database comprising five transactions. By setting the minimum

 

3. Most banks and financial institutions offer a wide variety of banking, investment, and credit services (the latter include business, mortgage, and automobile loans and credit cards). Some also  offer  insurance  and  stock  investment  services.  Financial  data collected in the banking and financial industry are often relatively complete, reliable, and of high quality, which facilitates systematic data analysis and applying machine learning algorithms.

Discuss below cases for this domain:

1. Classification and clustering of customers for targeted marketing. (5 Marks)

Ans 3a.

Introduction

In the banking and financial industry, data analysis and machine learning algorithms are crucial for enhancing customer relationships and optimizing services. One prominent application is the classification and clustering of customers for targeted marketing. By leveraging comprehensive, high-quality financial data, banks can segment their customer base effectively, enabling personalized marketing strategies that cater to the unique needs and preferences of different customer groups. This targeted approach not only improves customer satisfaction and retention but also maximizes the efficiency and effectiveness of marketing efforts,