MA3022 - Data Mining and Prototypes Neural Networks

Assignment Task

Theoretical background

• Give a description of classification and clustering problems. 
• What is the difference between them?
• Describe KNN approach and Hart’s algorithm for data reduction.
• Describe the K-means algorithm.

Project

1. Condensed Nearest Neighbour for data reduction in Nearest Neighbour classifier

Task

• Study how the number of prototypes depends on the number of points for two convex well-separated classes.
• Prepare a series of examples with more sophisticated non-convex shapes of well-separated classes. Study how the number of prototypes depends on the number of points in these classes.
• Study how the number of prototypes and outliers depends on the number of points for two well-separated classes with added background uniformly distributed noise (option “random”).
• In conclusion, discuss the results and propose a hypothesis for further study. Do not forget to save and submit the configurations of the classes and prototypes as figures!

2. Dynamics of k-means clustering

• Exploration Find the final k-means configurations for a series of datasets and various initial generations of centroids. How many different configuration did you observe? How frequently did they appear? How many iterations were required?
• Formulate a hypothesis about a number of different final k-means configurations and their frequencies. Analyse, how they depend on the number of data points. Check this hypothesis on the random sets of equidistributed points.
• Formulate a hypothesis about the convergence rate of k-means and its dependence on the number of data points. Check this hypothesis on the random sets of equidistributed points (use the same series of experiments as in question 2).
• In conclusion, discuss the results and propose a hypothesis for further study.

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