Data Mining (IN)

Multiple Choice

The following statements about data mining are true, except:

• Data mining contains the technique of finding the desired trend or pattern in a large database to assist decision making.

• Data mining only deals with databases and has nothing to do with statistics or artificial intelligence

• Data mining is used in databases with very large volumes of data

• Data mining is part of the Knowledge Discovery in Databases (KDD) process

Multiple Choice

The following statements about data warehouses are true, except:

• Data warehouse is a relational database management system.

• A data warehouse is a centralized data repository that can be queried for business purposes

• Data warehouse is useful for providing data that is ready to be transformed and processed by decision-making tools such as data mining.

• The data warehouse contains a variety of raw data from various sources without any data cleaning process.

Multiple Choice

Which of the following activities is not included in the data mining work

• Segmentation of a company's customers according to their income

• Calculating the total sales of a company

• Find loyal customers who use the company's products for a certain period of time

• Predicting the company's stock of goods in the future using historical records

Poll

One of the steps in the KDD process is the selection of the dataset that will be used to solve certain problems. If we want to know the group of credit card users who have problems or make fraudulent transactions based on the database of credit card usage transactions, the following attributes are required in the process, except

• Name of the bank providing the credit card

• Purchased product

• Purchase date

• Credit repayment date

Multiple Choice

The following data mining techniques or algorithms are predictive, except:

• Anomali detection

• Association

• Classification

• Regression

Multiple Choice

The following are problems that can be solved using one of the data mining techniques, namely classification, except:

• Determine which consumers are eligible to send the company's product offerings to reduce the 'cost' of correspondence

• Determine consumers who are not loyal and will use products from competitors.

• Predict certain products that will be purchased by certain consumers in the future based on previous sales data.

• Looking for consumers who make 'cheat' transactions in using credit cards

Multiple Choice

If we want to know the pattern of consumer shopping in a store, with the aim of determining the placement of goods in the store, then we use data mining techniques:

• Clustering

• Association

• Classification

• Regression

Multiple Choice

If we want to divide the market into different subsets of customers where a subset may be selected as a target market that is reached by a different marketing combination, then we use data mining techniques:

• Clustering

• Association

• Classification

• Regression

Multiple Choice

If we want to predict the number of new product sales based on promotional/advertising spending, then we use data mining techniques

• Clustering

• Association

• Classification

• Regression

Multiple Choice

Challenges in data mining techniques that must be completed include the following except:

• Very large amount of data (scalability)

• Data dimensionality problem

• Homogeneous data.

• Data quality problem