It was quite difficult to understand what you need. Anyway, I've got it. I guess you need to much all the terms to each sentence. So I think I've done it right. Check it out:
1.an electronic index of books - <span>B. computer catalog
</span><span>
2.a device which categorizes and locates web sites - </span><span>H. search engine
</span><span>
3.to draw a conclusion - </span>D. infer<span>
4.a block of information stored in an HTML file on a server - </span><span>A. web page
</span><span>
5.the table of contents of a web site - </span><span>G. home page
</span><span>
6.a software package which retrieves information from any or all available Internet servers - </span><span>I. browser
</span><span>
7.a highlighted word or phrase within a web page which acts as a "bridge" to another web page or site - </span><span>F. hyperlink
</span><span>
8.a topic sentence - </span><span>C. key sentence
</span><span>
9.a term which aids in narrowing a web search - </span>E. keyword
Answer:
Step 1: Divide (232)
successively by 2 until the quotient is 0:
232/2 = 116, remainder is 0
116/2 = 58, remainder is 0
58/2 = 29, remainder is 0
29/2 = 14, remainder is 1
14/2 = 7, remainder is 0
7/2 = 3, remainder is 1
3/2 = 1, remainder is 1
1/2 = 0, remainder is 1
Step 2: Read from the bottom (MSB) to top (LSB) as 11101000.
So, 11101000 is the binary equivalent of decimal number 232
(Answer).
K-means can be used for hierarchical clustering by creating a hierarchical tree structure. This is done by setting the number of clusters to be created, and then running the k-means clustering algorithm for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree. This process is repeated until the desired number of clusters has been created.
<h3>The Use of K-Means Clustering for Hierarchical Clustering</h3>
K-means clustering is a popular technique used in machine learning and data mining for partitioning data into clusters. It is a flat clustering algorithm, in which data points are grouped according to their similarity. While k-means clustering is suitable for partitioning data into a fixed number of clusters, it can also be used for hierarchical clustering. Hierarchical clustering is a clustering technique that creates a hierarchical tree structure, where each level of the tree is made up of clusters created by the k-means clustering algorithm.
The process of creating a hierarchical tree structure using k-means clustering is fairly straightforward. First, the number of clusters to be created is set, and then the k-means clustering algorithm is run for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree until the desired number of clusters has been created. This process ensures that the clusters created are meaningful and have similar characteristics.
Learn more about k-means :
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For learning HTML we recommend a simple text editor like Notepad (PC) or TextEdit (Mac).
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Answer:
Syntax errors are not able to occur in block based code, so they cannot be transitioned from block to text and can cause issues.
