Data Mining

True or False Data mining can be very useful in detecting patterns such as credit card fraud, but is of little help in improving sales.

False

Knowledge extraction, pattern analysis, data archaeology, information harvesting, pattern searching, and data dredging are all alternative names for ________.

data mining

Because of its successful application to retail business problems, association rule mining is commonly called ________.

market basket analysis

Which of the following is a data mining myth? 17) ______ A) Data mining requires a separate, dedicated database. B) The current state-of-the-art is ready to go for almost any business. C) Newer Web-based tools enable managers of all educational levels to do data mining. D) Data mining is a multistep process that requires deliberate, proactive design and use.

A) Data mining requires a separate, dedicated database.

Understanding customers better has helped Amazon and others become more successful. The understanding comes primarily from 6) _______ A) analyzing the vast data amounts routinely collected. B) developing a philosophy that is data analytics-centric. C) collecting data about customers and transactions. D) asking the customers what they want.

A) analyzing the vast data amounts routinely collected.

________ was proposed in the mid-1990s by a European consortium of companies to serve as a nonproprietary standard methodology for data mining.

Crisp-dm

The ________ is the most commonly used algorithm to discover association rules. Given a set of itemsets, the algorithm attempts to find subsets that are common to at least a minimum number of the itemsets.

Apriori

Which data mining process/methodology is thought to be the most comprehensive, according to kdnuggets.com rankings? 13) ______ A) SEMMA B) CRISP-DM C) proprietary organizational methodologies D) KDD Process

B) CRISP-DM

Which broad area of data mining applications analyzes data, forming rules to distinguish between defined classes? 10) ______ A) clustering B) classification C) visualization D) associations

B) classification

The data field "ethnic group" can be best described as 8) _______ A) interval data. B) nominal data. C) ordinal data. D) ratio data

B) nominal data.

Clustering partitions a collection of things into segments whose members share 12) ______ A) dissimilar collection methods. B) similar characteristics. C) similar collection methods. D) dissimilar characteristics.

B) similar characteristics.

Which broad area of data mining applications partitions a collection of objects into natural groupings with similar features? 11) ______ A) visualization B) classification C) clustering D) associations

C) clustering

All of the following statements about data mining are true EXCEPT 7) _______ A) the valid aspect means that the discovered patterns should hold true on new data. B) the potentially useful aspect means that results should lead to some business benefit. C) the process aspect means that data mining should be a one-step process to results. D) the novel aspect means that previously unknown patterns are discovered.

C) the process aspect means that data mining should be a one-step process to results.

In data mining, finding an affinity of two products to be commonly together in a shopping cart is known as 16) ______ A) decision trees. B) artificial neural networks. C) cluster analysis. D) association rule mining

D) association rule mining

A data mining study is specific to addressing a well-defined business task, and different business tasks require 9) _______ A) general organizational data. B) general economic data. C) general industry data. D) different sets of data.

D) different sets of data.

What does the scalability of a data mining method refer to? 15) ______ A) its speed of computation and computational costs in using the mode B) its ability to predict the outcome of a previously unknown data set accurately C) its ability to overcome noisy data to make somewhat accurate predictions D) its ability to construct a prediction model efficiently given a large amount of data

D) its ability to construct a prediction model efficiently given a large amount of data

What does the robustness of a data mining method refer to? 14) ______ A) its speed of computation and computational costs in using the mode B) its ability to construct a prediction model efficiently given a large amount of data C) its ability to predict the outcome of a previously unknown data set accurately D) its ability to overcome noisy data to make somewhat accurate predictions

D) its ability to overcome noisy data to make somewhat accurate predictions

Data preparation, the third step in the CRISP-DM data mining process, is more commonly known as ________.

Data preperation or per-processing

True or False Market basket analysis is a useful and entertaining way to explain data mining to a technologically less savvy audience, but it has little business significance.

False

True or False During classification in data mining, a false positive is an occurrence classified as true by the algorithm while being false in reality.

True

True or False In data mining, classification models help in prediction

True

True or False Using data mining on data about imports and exports can help to detect tax avoidance and money laundering.

True