Homework
Part I
Answer the following questions in a point by point fashion.
• For sparse data, discuss why considering only the presence of non-zero values might give a more accurate view of the objects than considering the actual magnitudes of values. When would such an approach not to be desirable?
• Describe the change in the time complexity of K-means as the number of clusters to be found increases.
• Discuss the advantages and disadvantages of treating clustering as an optimization problem. Among other factors, consider efficiency, non-determinism, and whether an optimization-based approach captures all types of clusterings that are of interest.
• What is the time and space complexity of fuzzy c-means? Of SOM? How do these complexities compare to those of K-means?
• Explain the difference between likelihood and probability.
• Give an example of a set of clusters in which merging based on the closeness of clusters leads to a more natural set of clusters than merging based on the strength of the connection (interconnectedness) of clusters.
Part II
Consider the mean of a cluster of objects from a binary transaction data set.
• What are the minimum and maximum values of the components of the mean?
• What is the interpretation of components of the cluster mean?
• Which components most accurately characterize the objects in the cluster?
Format your homework according to the give formatting requirements:
• The answer must be using Times New Roman font (size 12), double spaced, typed, with one-inch margins on all sides.
• The response also includes a cover page containing the student's name, the title of the homework, the course title, and the date. The cover page is not included in the required page length.
• Also include a reference page. The references and Citations should follow APA format. The reference page is not included in the required page length.