Data Mining Clustering Analysis: “Basic Concepts and Algorithms”   and “Additional Issues and Algorithms” Assignment

1) Explain the following types of Clusters:

· Well-separated clusters

· Center-based clusters

· Contiguous clusters

· Density-based clusters

· Property or Conceptual

2) Define the strengths of Hierarchical Clustering and then explain the two main types of Hierarchical Clustering.

3) DBSCAN is a dentisy-based algorithm. Explain the characteristics of DBSCAN.

4) 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?

5) Describe the change in the time complexity of K-means as the number of clusters to be found increases.

6) 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.