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Multidimensional Scaling in Data Analysis: Definition & Examples

Instructor: David Gloag
In this lesson, we'll take a look at multidimensional scaling in data analysis and how the two are related. You'll see how we use multidimensional analysis to organize and better understand information.

Sifting Through the Clutter

We consume a lot of information, although we might not realize it. Television newscasts keep us abreast of the things that are happening around us, various websites provide us with topic-specific information, and even our cell phones bring us interesting tidbits from our friends and family. But by the time that information is ready for our consumption it has been massaged. That makes sense as the raw information would be hard to digest. Can you imagine trying to see temperature trends without the corresponding graph, or the fluctuations in the stock market? Most of us can't. Without those adjustments, the raw information would simply be clutter. So, we employ a number of techniques to help us makes sense of everything. One of those is multidimensional scaling.

What is Multidimensional Scaling?

Multidimensional scaling is the process of fitting a set of unknown or unfamiliar values into a familiar framework. This fitting ensures the proportions of the values remain the same when compared to each other. The purpose is to allow comparisons of the values to occur against something that is known. As an example, think back to the temperature graph mentioned above. When you look at the graph, are you looking at the values, or are you comparing the values to something like room temperature, or the freezing point of water? Likely, you're making comparisons. That's because the comparison gives us more information than the straight values would on their own. The comparison gives us context.

What is Data Analysis?

Data analysis is an examination process, meaning that various techniques are applied to information with the purpose of deriving some sort of conclusion from that information. For example, say you wanted to determine the average age of a group of individuals. You might add all of their ages up, and divide by the number of individuals. You arrived at a conclusion (average age) using two mathematical operations or techniques (add, divide) to get to that conclusion. Clearly, this example is rather simple. But it doesn't take much to see how this can be extended using other situations and techniques.

How Are Multidimensional Scaling and Data Analysis Related?

Multidimensional scaling is an example of data analysis technique. And like adding and dividing above, you can apply it to an input information set. The result of that process will tell you something about the information set that you didn't already know. Consider the temperature graph mentioned above. The second you bring the known framework into play, like room temperature, you immediately know whether you need a sweater or jacket if you go outside or whether it's time to break out the winter boots.

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