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.
Below are the different level of analysis of data mining. Provide a brief description of each:
1) Artificial Neural Networks
2) Genetic algorithms
3) Nearest neighbor method
4) Rule induction
5) Data visualization
You must make at least two substantive responses to your classmates’ posts. Respond to these posts in any of the following ways:
· Build on something your classmate said.
· Explain why and how you see things differently.
· Ask a probing or clarifying question.
· Share an insight from having read your classmates’ postings.
· Offer and support an opinion.
· Validate an idea with your own experience.
· Expand on your classmates’ postings.
· Ask for evidence that supports the post.
Discussion Length (word count): At least 150 words