Data Mining Homework Assignment
Check before you submit. Get Turnitin Score Report in 15 Minutes.
Don't risk the 'Red' score. Get the exact same Turnitin report your professor uses. Join 50,000+ students who submitted their essays with confidence this semester.
1. What’s an attribute? What’s a data instance?
What’s noise? How can noise be reduced in a dataset?
Define outlier. Describe 2 different approaches to detect outliers in a dataset.
Describe 3 different techniques to deal with missing values in a dataset. Explain when each of these techniques would be most appropriate.
Given a sample dataset with missing values, apply an appropriate technique to deal with them.
Give 2 examples in which aggregation is useful.
Given a sample dataset, apply aggregation of data values.
What’s sampling?
What’s simple random sampling? Is it possible to sample data instances using a distribution different from the uniform distribution? If so, give an example of a probability distribution of the data instances that is different from uniform (i.e., equal probability).
What’s stratified sampling?
What’s “the curse of dimensionality”?
Provide a brief description of what Principal Components Analysis (PCA) does. [Hint: See Appendix A and your lecture notes.] State what’s the input and what the output of PCA is.
What’s the difference between dimensionality reduction and feature selection?
Describe in detail 2 different techniques for feature selection.
Given a sample dataset (represented by a set of attributes, a correlation matrix, a co-variance matrix, …), apply feature selection techniques to select the best attributes to keep (or equivalently, the best attributes to remove).
What’s the difference between feature selection and feature extraction?
Give two examples of data in which feature extraction would be useful.
Given a sample dataset, apply feature extraction.
What’s data discretization and when is it needed?
What’s the difference between supervised and unsupervised discretization?
Given a sample dataset, apply unsupervised (e.g., equal width, equal frequency) discretization, or supervised discretization (e.g., using entropy).
Describe 2 approaches to handle nominal attributes with too many values.
Given a dataset, apply variable transformation: Either a simple given function, normalization, or standardization.
Definition of Correlation and Covariance, and how to use them in data pre-processing (see pp. 76-78)
Data Mining Homework Assignment Essay
DO MY HOMEWORK NOW!Write my Essay. Premium essay writing services is the ideal place for homework help or essay writing service. if you are looking for affordable, high quality & non-plagiarized papers, click on the button below to place your order. Provide us with the instructions and one of our writers will deliver a unique, no plagiarism, and professional paper.
Get help with your toughest assignments and get them solved by a Reliable Custom Papers Writing Company. Save time, money and get quality papers. Buying an excellent plagiarism-free paper is a piece of cake!
All our papers are written from scratch. We can cover any assignment/essay in your field of study.
