progamming in matlab - 03/11/2016 21:12 EDT
$10-30 USD
Paid on delivery
PART1
1. Implementing the modified K-Means++ and fuzzy C-Means clustering methods using MATLAB.
- Write functions that take a data set and generate clusters, the number of which is specified by the user.
- The input set of instances can be of two dimensions or of more than two dimensions.
- The output from the main function must be the cluster assignments of the input instances.
2. Implementing Expectation Maximization method using MATLAB.
- Write functions that take a set of instances and generate a number of clusters.
- The input set of instances can be of two dimensions or of more than two dimensions.
- The output from the main function must be the cluster assignments of the input instances.
3. Rewrite functions to generate synthetic data that allow adding Gaussian noise to the instances.
- The functions should be able to allow user to specify the parameters of Gaussian noise, i.e., mean and variance.
- Use the examples generated from the functions to evaluate the three methods.
- Experiment with different parameters of the three methods.
NOTE:
a. There is no requirement to develop a GUI to visualize the intermediate or the final results in problems 1 and 2, although a visualization is recommended.
b. The synthetic data generated using the functions from problem 3 (above) are suggested to be used for evaluating the two methods from problems 1 and 2.
A report that includes the following items is due in addition to the source code for the functions:
- A description of each function,
- how to run the functions to get the reported results, and
- the experimental results.
PART2
1. Evaluate PCA and ISOMAP.
- Get a copy of PCA and ISOMAP implementation in MATLAB.
- Download 3 data sets from UCI repository. Note that the dimensionality of each data set is preferably greater than 10.
- Evaluate the PCA and ISOMAP method with the data sets. Be creative how to evaluate and discuss your observations.
2. Design a mini project and use the methods taught in this class (but not limited to those methods) to achieve a clustering or classification objective.
- Make necessary changes to the code and implement additional ones when needed.
- Evaluate the programs with either synthetic data sets or real-world data sets.
- Write a report to discuss the problem, the implementation, and the results.
A report that includes the following items is due in addition to the source code for the functions:
- A description of each function,
- how to run the functions to get the reported results, and
- discussion of the experimental results.
Project ID: #11976341
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