50 Questions
What is the main focus of data mining in the context of business intelligence?
Recognizing the wide range of applications of data mining
Which term refers to the standardized data mining processes discussed in the chapter?
CRISP-DM
What is a key objective of business analytics and data mining?
Deriving meaningful insights from data
What is emphasized in the opening vignette 'Data Mining Goes to Hollywood'?
Answering and discussing case questions related to data mining
What is a significant aspect when considering commercial versus free/open source data mining software tools?
Awareness of the advantages and disadvantages of each type of software
What is a key learning objective related to data mining discussed in Chapter 5?
Learning the standardized data mining processes like CRISP-DM, SEMMA, KDD, etc.
What is the typical classification problem in the context of data mining?
MPAA Rating
According to the given data, what are the three categories of competition?
High, Medium, Low
What is the range of the number of possible values for the genre in the given data?
10
In the context of data mining, what is the most critical ingredient for DM?
Data
What is the definition of data mining according to Fayyad et al. (1996)?
Extraction of valid and understandable patterns from data
What is the lowest level of abstraction from which information and knowledge are derived in data mining?
"Data"
What does data mining extract from data?
"Patterns"
Which learning method uses Decision trees and ANN/MLP in the context of data mining?
"Classification"
What type of analysis is K-means used for in data mining?
"Clustering"
"Discovery-driven data mining" is an example of which type of DM?
"Knowledge discovery"
"Automating the loan application process" is an application of data mining in which industry?
"Banking"
What are some other names for data mining according to Fayyad et al. (1996)?
All of the above
What is the purpose of cluster analysis in data mining?
To automatically identify natural groupings of things
Which method employs unsupervised learning and is used to find interesting relationships between variables?
Association rule mining
What is the main difference between divisive and agglomerative methods in cluster analysis?
Approach to combining clusters
Which algorithm employs the divide and conquer method for building decision trees?
ID3
What does the Gini index determine in the context of decision trees?
The purity of a specific class as a result of a decision to branch along a particular attribute/value
Which clustering method uses statistical methods including hierarchical and non-hierarchical approaches?
k-means clustering
What is the purpose of association rule mining in business?
To find interesting relationships between variables (items or events)
"How many clusters does k-means clustering algorithm pre-determine?"
"Number of clusters = (n/2)1/2 (n: no of data points)"
Which of the following is NOT a representative application of association rule mining?
Predicting stock market trends
In association rule mining, what does the support value represent in the generic rule X Y [S%, C%]?
How often X and Y go together
Which algorithm uses a bottom-up approach to find subsets that are common to at least a minimum number of the itemsets?
Apriori
What is the main purpose of the Apriori algorithm in association rule mining?
Identifying frequent item sets
Which software is NOT listed as a commercial data mining tool?
Weka (now Pentaho)
What is a common myth about data mining according to the text?
It provides instant solutions/predictions
What is one of the common data mining mistakes according to the text?
Ignoring suspicious findings and quickly moving on
According to the text, what is another common data mining mistake?
Being sloppy about keeping track of the data mining procedure and results
What is emphasized as one of the pitfalls in data mining according to the text?
Naively believing everything you are told about the data
Which of the following is NOT provided as an application of association rule mining?
Forecasting weather patterns
What is one of the mistakes highlighted in data mining according to the text?
Selecting only aggregated results and not individual records/predictions
According to the text, what is one of the common myths about data mining?
It requires a separate, dedicated database
What is the primary focus of data mining applications in the retailing and logistics industry?
Optimizing inventory levels at different locations
What is a critical task in the data preparation phase of the data mining process?
Data integration
Which industry is NOT mentioned as a highly popular application area for data mining?
Financial services
What is the main purpose of classification in data mining?
To classify new data based on past data
Which assessment method for classification focuses on transparency and explainability?
Interpretability
In a classification problem, what does the True Positive Rate measure?
The probability of correctly identifying positive cases
What is the purpose of k-Fold Cross Validation in estimation methodologies for classification?
To aggregate test results for true estimation of prediction accuracy
Which method is NOT mentioned as an estimation methodology for classification?
Training model assessment
What does the ROC curve measure in assessment methodologies for classification?
True positive rate (sensitivity) versus false positive rate (1-specificity)
What does the Data Reduction task aim to do in the data preparation phase?
Reduce number of variables and cases
What is NOT a common standard process for conducting data mining projects?
KDNuggets (Knowledge Discovery Nuggets)
What is a primary focus of data mining applications in the insurance industry?
Forecast claim costs for better business planning
Test your knowledge on topics such as optimizing inventory levels, store layout, logistics predictions, machinery failures, production anomalies, and product quality improvement.
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