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Questions and Answers
What is the main function of a Web search engine?
What are the possible types of hits a user may receive when using a Web search engine?
In the context of data mining, what is the purpose of basket data analysis?
Which field does biological network analysis fall under according to the text?
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What does the text mention as a reason for utilizing data mining in software engineering?
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What does the text refer to when discussing 'invisible data mining'?
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What is data mining also known as?
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Which step in the knowledge discovery process involves removing noise and inconsistent data?
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What does the data ink ratio measure in data visualization?
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In the information industry, what is the common trend related to data cleaning and data integration?
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What is one of the uses of data visualization?
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Which step involves consolidating data into appropriate forms through summary or aggregation operations?
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Which type of chart is recommended in Excel for visualizing trends over time?
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What is the essential process in the knowledge discovery process where intelligent methods are used to extract data patterns?
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What is a recommended design principle for tables in data visualization?
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Which step in the knowledge discovery process identifies truly interesting patterns based on interestingness measures?
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What is the purpose of visualizing data using Parallel Coordinates?
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Which type of chart is not recommended in the text for effective data visualization?
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What is an aspect of advanced data visualization discussed in the text?
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How are the axes in Parallel Coordinates scaled?
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What is the primary feature of Icon-Based Visualization Techniques?
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What does the technique of Chernoff Faces primarily aim to display?
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How do Parallel Coordinates represent each data item?
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Which technique uses shape, color icons, and tile bars for visualization?
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What is the primary purpose of a Data Warehouse?
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What distinguishes a Data Mart from a Data Warehouse?
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How do dependent data marts differ from independent data marts?
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What is the typical range of size for a Data Warehouse?
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Why are Data Marts categorized as independent or dependent?
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Study Notes
Data Visualization
- Data visualization can be as simple as creating a summary table or generating charts to help interpret, analyze, and learn from the data.
- Uses of data visualization include identifying data errors and reducing the size of the dataset by highlighting important relationships and trends.
Landscapes
- Landscapes visualize news articles as a landscape, transforming data into a 2D spatial representation that preserves the characteristics of the data.
Parallel Coordinates
- Parallel coordinates visualize data by using n equidistant axes that correspond to the attributes, with each data item represented as a polygonal line intersecting each axis.
Icon-Based Visualization Techniques
- Icon-based visualization techniques include:
- Chernoff Faces: display variables on a 2D surface using facial features.
- Stick Figures: use human body features to represent data.
- Shape coding: use shape to represent certain information.
- Color icons: use color icons to encode more information.
- Tile bars: use small icons to represent features in document retrieval.
Data Mining
- Data mining, also known as knowledge discovery from data, is a knowledge discovery process that involves data cleaning, integration, selection, transformation, pattern discovery, pattern evaluation, and knowledge presentation.
- Applications of data mining include:
- Web page analysis
- Collaborative analysis and recommender systems
- Basket data analysis and targeted marketing
- Biological and medical data analysis
Data Warehouse
- A data warehouse is a repository that provides corporate-wide data integration, typically containing detailed and summarized data.
- Data warehouse models include:
- Enterprise data warehouse
- Data mart: a subset of corporate-wide data that is of value to a specific group of users, sourced from operational systems or external information providers.
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Description
Learn about data mining, which is the process of extracting knowledge and insights from data. This quiz covers topics like data cleaning, integration, selection, transformation, pattern discovery, evaluation, and knowledge presentation.
