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alexgriva [62]
4 years ago
12

What is the technique used to divide information sets into mutually exclusive groups such that the members of each group are as

close together as possible to one another and the different groups are as far apart as possible?
1. Statical detection
2. Cluster analysis
3. Association detection
4. None of these.
Computers and Technology
1 answer:
Sphinxa [80]4 years ago
0 0

Answer:

The correct answer to the following question will be Option 2 (Cluster analysis).

Explanation:

  • The process of sorting a collection of items in such a manner that objects within the same category are much more identical than objects in many other clusters, known as Cluster analysis.
  • The goal of this analysis would be to create similar groups of topics where "commonality" means a global metric across the entire set of characteristics between each pair of subjects.

The other options, Option 1 and Option 3 would not be able to create common motifs, such groups. Therefore, the correct answer is Option 2.

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QveST [7]

Answer:

Advantages of Digital Multimeters:

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2)They reduce reading and interpolation errors.

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4)Parallax errors are eliminated.

I hope this helps.

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3 years ago
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djverab [1.8K]

Answer:

The correct answer is d) all of the above

Explanation:

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3 years ago
PLZZ HELP
kap26 [50]

Answer:

Explanation:

Keep in mind a lossy algorithm will lose information while a lossless algorithm maintains all your original information.

Therefore:

A. False, a lossy algorithm will not allow perfect reconstruction.

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C. False, you can use a lossless algorithm for anything.

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3 years ago
Complete the concept map on computer as outlined below​
USPshnik [31]

Answer:

Here is your answer.

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A data analyst creates a data frame with data that has more than 50,000 observations in it. When they print their data frame, it
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A list is an R-object that can have a wide range of different items inside of it, including vectors, functions, and even another list.

The head() function lets you display the first observations in a data frame. The method tail() prints the final observations in your data collection in a manner similar to that. Both head() and tail() print a top line called the 'header', which contains the names of the distinct variables in your data collection. The R language's transform() function is used to alter data. The first parameter is transformed into a data frame. A suitable variable name comprises of letters, numbers and the dot or underline characters. The variable name does not begin with a number but rather with a letter or a dot.

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