I’m studying for my Computer Science class and need an explanation.
Task 1
Data representation is the act displaying the visual form of your data. The process of identifying the most effective and appropriate solution for representing our data is unquestionably the most important feature of our visualization design. Working on this layer involves making decisions that cut across the artistic and scientific foundations of the field.
Here we find ourselves face-to-face with the demands of achieving that ideal harmony of form and function that was outlined in Chapter 6, Data Representation. We need to achieve the elegance of a design that aesthetically suits our intent and the functional behavior required to fulfill the effective imparting of information.
According to Kirk 2019, in order to dissect the importance of data representation, we are going to “look at it from both theoretical and pragmatic perspectives.” Choose three of the storytelling techniques from the gallery of charts (Pages 140 – 188) in which data is presented and stories are being interpreted. Discuss the importance and advantages of using these techniques. Provide an example of each technique.
Need 300 words
Reference
Kirk, A. (2019). Data Visualization: A Handbook for Data-Driven Design. 2nd Ed. Thousand Oaks, CA: Sage Publications, Ltd.
Reply Post
When replying to a classmate, offer your opinion on what they posted and discuss the important advantage of their selected technique.
Also, using at least 3 – 5 sentences, are the examples, in your opinion, relevant and usable?
Task 2
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Data Mining Clustering Analysis: Basic Concepts 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.List and Explain the three types of measures associated with Cluster Validity.
- 5.In regards to Internal Measures in Clustering, explain Cohesion and Separation
Need elobrated answer for each question



