Q1.Select two application areas for data mining NOT discussed in the text book a
ID: 3863279 • Letter: Q
Question
Q1.Select two application areas for data mining NOT discussed in the text book and briefly discuss how data mining is being used to solve a problem (or to explore an opportunity)?
Q2.. What is Association Rule Mining? And explain how Market-basket analysis helps retail business to maximize the profit from business transactions?
Q3.Discuss k-nearest Neighbor (KNN) learning algorithm. What is the significance of the value of k in k-NN.
Q4.Discuss the two estimation methods of classification-type data mining models while considering ANN as a classifier.
Explanation / Answer
Q1.
The two application areas for data mining is given below:
Biological Data Analysis
The contribution of data mining in the field of biological data analysis is explained below:
Retail Industry Ecommerce Website
Data mining contributes a large scale of analysis under the category of retail industry, by analyzing the sales, products, transportation of goods etc.
Naturally the collected data will expand rapidly because of the availability of the web or the increasing of ease.
The data mining is very useful in retain industry, to identify the pattern of purchasing of products by the customers.
This will help the industry to improve the quality of the products, so that it will satisfy the customers.
The contribution of data mining in the field of Retail Industry Ecommerce Website
is explained below:
Q2.
Association rule mining refers to a procedure, method or a process which is used to identify the patterns, co-relations or the structure form the set of the data.
These data are there in some databases.
And these databases can be relational databases, transactional databases etc.
Usage of association rule in the market based analysis is explained below:
Q3.
KNN algorithm:
K in KNN:
Q4.
The two estimation methods of classification-type data mining models while considering ANN as a classifier are given as follows:
The Backpropagation learning algorithm.
Example: Multilayer perception.
Genetic Algorithm:
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