Order ID:89JHGSJE83839 | Style:APA/MLA/Harvard/Chicago | Pages:5-10 |
Instructions:
Cluster Analysis Paper Using Data Mining
In a point-by-point manner, respond to the following questions. Fill in the blanks with your answers to each question. This is not an essay. With any content included into the assignment, please use the Author, YYYYY APA citations.
1. Explain why evaluating merely the presence of non-zero values, rather than the exact magnitudes of values, could yield a more realistic depiction of the objects in sparse data. When is it not advisable to take this approach?
2. Explain how the temporal complexity of K-means changes as the number of clusters to be discovered grows.
3. Compare and contrast the benefits and drawbacks of considering clustering as an optimization problem. Examine efficiency, non-determinism, and whether an optimization-based approach catches all forms of clusterings of relevance, among other things.
4. How complex are fuzzy c-means in terms of time and space? What is the meaning of SOM? What are the differences between these complexity and those of K-means?
5. Distinguish between the terms “likelihood” and “probability.”
6. Describe a set of clusters in which merging clusters based on their closeness results in a more natural set of clusters than merging clusters based on their strength of connection (interconnectedness).
Requirement:
Provide a questions and answers sheet with six (6) questions that must be answered in order.
You must utilize APA in-text citations and scholarly references that are correctly formatted. NO COPYING AND PASTEING FROM THE INTERNET OR FROM ANOTHER STUDENT’S PAPER. Plagiarism cannot be undone.
RUBRIC |
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Excellent Quality 95-100%
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Introduction
45-41 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned. |
Literature Support 91-84 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned. |
Methodology 58-53 points Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met. |
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Average Score 50-85% |
40-38 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided. |
83-76 points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration. |
52-49 points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met. |
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Poor Quality 0-45% |
37-1 points The background and/or significance are missing. No search history information is provided. |
75-1 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration. |
48-1 points There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met |
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Cluster Analysis Paper Using Data Mining