Description
In chapter 8 we focus on cluster analysis. Therefore, after reading the chapter answer the following questions:
- What are the characteristics of data?
- Compare the difference in each of the following clustering types: prototype-based, density-based, graph-based.
- What is a scalable clustering algorithm?
- How do you choose the right algorithm?
No Plagiarism. APA Format. 600 Words
Explanation & Answer
View attached explanation and answer. Let me know if you have any questions.
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Data Mining
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Data Mining
1.
One of the data characteristics that influence cluster examination is high dimensionality.
In high-dimensional data pacts, the traditional Euclidean density idea, which refers to the
numeral of points for each element volume, turn out to be worthless. The second characteristic is
size. a lot of clustering algorithms working effectively for minute or medium-size data pacts are
incapable of handling more extensive data sets. The third characteristic is sparseness. Thin data
frequently has asymmetric characteristics, where zero figures are not essential as non-zero ones
(Tan, Steinbach & Kumar, 2016). The other characteristic is noise and outliers. An outlier can
frequently decrease the clustering algorithms’ performance, particularly algorithms like K-means
that are prototype-b...
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