Section 1: Answer the following questions no word count is required but the questions need to be answered. Use at least two one peer reviewed, scholarly journal references. Data Mining Clustering Analysis: Basic Concepts and Algorithms Assignment 1) Explain the following types of Clusters: 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) List and Explain the three types of measures associated with Cluster Validity. 5) In regards to Internal Measures in Clustering, explain Cohesion and Separation. Section 2: Answer the following question What is classification and why is it needed in Data Mining? Discussion Length (word count): At least 200 words References: At least one peer-reviewed, scholarly journal references. No plagiarism Requirements: As needed Well-separated clusters Center-based clusters Contiguous clusters Density-based clusters Property or Conceptual
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