IS 335 Chapter 5 Practice Questions
Quiz yourself by thinking what should be in
each of the black spaces below before clicking
on it to display the answer.
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| show | Process of creating new insights from information
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| Organizations relying on business analytics make extensive use of... | show 🗑
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| show | 1) Discovery by using existing information about how decisions are made in order to build systems that make similar decisions
2) Discovery by finding useful patterns in observations, typically embodied in explicit data
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| show | Commonly known as Data Mining (DM), it is the gold mined from the databases refers to patterns buried between the data variables, which represent new insights about the previously unknown relationships among theses variables
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| Define Knowledge Discovery in Databases: | show 🗑
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| show | 1) Exploding data volumes
2) Increasing decision complexity
3) Need for quick reflexes
4) Technological progress
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| show | Ready for analysis, marked by the convergence of improved and inexpensive data storage capabilities, abundance of customer data resulting from the explosion of e-commerce applications
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| Increasing decision complexity results from... | show 🗑
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| show | Efficiently and effectively to environmental changes, with greater accountability for their actions.
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| show | Electronic Retailer
Ex) eBags, a web based storefront of handbags, suitcases, wallets, and other similar products
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| What are some examples of successful DM applications? | show 🗑
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| Define Cross-Industry Standard Process For Data Mining (CRISP-DM): | show 🗑
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| show | Process of cleaning and transforming raw data prior to processing and analysis. Important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data.
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| show | 1) Selection
2) Construction and transformation of variables
3) Data integration
4) Formatting
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| Step 1 (Selection) for Data Preparation involves... | show 🗑
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| show | Many of the variables involved in the DM model will need to be transformed or constructed from existing raw data. Specific model may require transformations that group raw data values in ranges such as low, medium, and high
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| Step 3 (Data integration) for Data Preparation involves... | show 🗑
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| show | Involves the reformatting and reordering of the data fields, as required by the DM model.
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| Define Model Building and Validation: | show 🗑
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| What is the most popular validation technique is.... | show 🗑
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| show | Divides the total validation dataset into ten approximately equal-sized datasets, using each of the ten validation sets a single time, to evaluate the model comparing the accuracy with that resulting from the using the remaining nine training sets.
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| Define Model Evaluation and Interpretation: | show 🗑
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| Define Deployment: | show 🗑
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| show | 1) Business Understanding
2) Data Understanding
3) Data Preparation
4) Modeling Building and Validation
5) Model Evaluation and Interpretation
6) Deployment
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| show | 50-80%
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| Define Data Collection: | show 🗑
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| Define Data Description: | show 🗑
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| Define Data Quality and Verification: | show 🗑
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| What is the first step in order to define which DM technique to use... | show 🗑
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| show | Understand the characteristics of the input and outcome data that will be used.
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| Define "To Describe What Happened": | show 🗑
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| Define Descriptive Techniques: | show 🗑
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| What are the different types of descriptive techniques? | show 🗑
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| What is the Affinity or Association Descriptive Technique? | show 🗑
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| show | Uncover the natural groupings of data that may not be as obvious through casual inspection. Goal is to create clusters of input records based on a set of characteristics that recognizes them as a similar
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| show | Apriori Association Rule Algorithm
Generalized Rule Induction (GRI)
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| Apriori Association Rule Algorithm is... | show 🗑
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| Define To Predict What Will Happen: | show 🗑
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| show | Three
Statistical
Connectionist
Rule Induction
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| show | Find how two or more variables are related to each other, the correlations between the variables.
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| show | Curve fitting, a method used to identify a mathematical equation that can describe the relationship between the input variables and the outcomes
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| show | Least square method
Nonlinear Correlation Method
Multivariate Correlation Techniques
Inferential Statistical Techniques
Statistical Techniques
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| show | Artificial Neural Networks (ANN) techniques.
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| show | Computer based representation of what is theorized to be the human brain's physiological structure. Used as a predictive technique or as a clustering technique.
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| show | They can learn
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| Define Memory-Based Reasoning: | show 🗑
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| Define Decision Tree and Rule Induction Methods: | show 🗑
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| Define Rule Induction: | show 🗑
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| show | Classification and Regression Tree, the most popular methods to build a decision search tree. Performs a binary split of a continuous variable at each node.
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| Define CHAID Method: | show 🗑
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| Define the Storage Law: | show 🗑
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| Define the Moore's Law: | show 🗑
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| Define Data Tombs: | show 🗑
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| show | 1) User expectations are too high
2) Putting the right tools in the wrong hands
3) Dishing up data that users need to figure out how to use
4) Training users only at the beginning of the project
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| What are the common mistakes that organizations seeking the deployment of DM techniques must avoid? | show 🗑
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| show | Refers to putting analytics into action. Necessary to put the results into action in order to drive business value
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| show | 1) Scaling analysis to large databases
2) Scaling to high-dimensional data and models
3) Automating the search
4) Finding patterns and models understandable and interesting to user
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| show | 1) Domestic Economy
2) Internationalization
3) Government
4) Finance
5) Infrastructure
6) Management
7) Science and Technology
8) People
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