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 be desirable?

I’m stuck on a Computer Science question and need an explanation.

  1. 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 be desirable?
  2. Describe the change in the time complexity of K-means as the number of clusters to be found increases.
  3. 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.
  4. What is the time and space complexity of fuzzy c-means? Of SOM? How do these complexities compare to those of K-means?
  5. Explain the difference between likelihood and probability.
  6. 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 connection (interconnectedness) of clusters.

Read chapter 8 and 9 and answer the above questions. Write a 2-3 page paper using minimum of 2 references from peer reviewed articles from google scholar. No Plagiarism.

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