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Questions and Answers
What is the purpose of discretization methods in numeric data?
What is the main characteristic of a Data Warehouse?
What is the primary goal of data summarization?
Which approach to clustering is based on the idea of dividing the data into a set of non-overlapping clusters?
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What is the key difference between the K-means Algorithm and the Hierarchical Approach?
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What is the primary advantage of using the Hierarchical Approach over the K-means Algorithm?
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What is the purpose of partitioning in data clustering?
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What is the primary goal of data warehousing?
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What is the key benefit of using data summarization in data analysis?
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What is the primary characteristic of a Data Warehouse?
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Study Notes
Data Reduction Strategies
- Dimensionality reduction removes unimportant attributes, and methods include wavelet transforms, PCA, and feature subset selection.
- Numerosity reduction (Data Reduction) techniques include regression and log-linear models, histograms, clustering, sampling, and data cube aggregation.
- Data compression is another strategy.
Principal Component Analysis (PCA)
- Finds a projection that captures the largest amount of variation in data.
- Projects original data onto a smaller space, resulting in dimensionality reduction.
Example of PCA
- Calculate the dot product of two vectors (d1 and d2) to find the cosine of the angle between them.
- Calculate the magnitude of each vector using the formula ||d|| = (sum of squares of each element)^(1/2).
Correlation
- Measures the linear relationship between objects.
- Computes correlation by standardizing data objects (p and q) and taking their dot product.
- Formula: correlation(p, q) = p' ∙ q'.
Data Exploration & Visualization
Summary Statistics
- Numbers that summarize properties of the data, including frequency, location, and spread.
- Examples: location - mean, spread - standard deviation.
- Most summary statistics can be calculated in a single pass through the data.
Frequency and Mode
- Frequency of an attribute value is the percentage of time the value occurs in the data set.
- For numeric data, use discretization methods.
Data Warehousing and On-line Analytical
What is a Data Warehouse?
- Defined in many different ways, but not rigorously.
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Description
Test your knowledge of data reduction strategies, including dimensionality reduction, numerosity reduction, and data compression. Learn about techniques such as PCA, wavelet transforms, and feature subset selection.
