Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.
1.3.1. The Human
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Из курса от партнера University of Illinois at Urbana-Champaign
Data Visualization
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Week 1: The Computer and the Human
In this week's module, you will learn what data visualization is, how it's used, and how computers display information. You'll also explore different types of visualization and how humans perceive information.
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John C. HartProfessor of Computer Science
Department of Computer Science
So in data visualization, we're using a computer to display data to a human.
And now we need to focus on how the human perceives that data.
And so, I'd like to spend the next few videos talking about the human,
which is sometimes a foreign subject to many computer science students.
We can use what's called the model human processor to describe how a human works in
terms of how a computer works.
So here's a computer.
This might be a diagram of a computer that might be computer in a cellphone,
for example.
You're going to have a CPU, some central processing unit.
And there'll be memory.
You'll have Cache memory and then RAM.
The Cache is sort of a short term memory that makes the slower RAM run faster for
the CPU because the Cache is faster.
You might have an output processor that generates graphics or
causes the phone to vibrate or do other things.
And there might be an input processor, some media processor
that takes the video input from a camera, and the audio input from a microphone.
And so we can use the same design to sort of understand how a human works.
And this is called the model human processor, and
it was invented by Stuart Card back in 1981
as a way of describing how humans work to people that work in with computers.
And so with the model of human processor, we don't have a CPU,
we have our cognitive processor.
And we don't have a media processor accepting video input and audio input,
we have eyes and ears that generate media in a Visual Image Store and
an Auditory Image Store that is then processed by a perceptual processor before
they're processed by the Cognitive Processor, the brain.
And we don't have cache and RAM, we have a working memory, and a long term memory.
And we don't have a graphics processor and
something that will vibrate a phone, we have a motor process for the controls,
how our hands work and for example how our hands might draw a picture for us.
And in each case these, each of these processes, memories and
processors have certain decay and cycle times,
storage sizes that we can discuss in the same way that we discuss storage sizes
of random access memory or the processing time of a processor.
So first we might look at video processing, visual processing.
We have an eye, and the eye takes 230 milliseconds to access.
Meaning we need something from the eye, we get it about 230 milliseconds later.
And that can vary between up to 70 and to 700 milliseconds.
So we can get things as fast as 70 milliseconds from the eye, right?
And then we have a Visual Image Store, and the size is about 17 and
we're going to talk not necessarily in terms of bits, bytes or words.
We're going to talk about letters or chunks.
And we can store about 17 things in our Visual Image Stores, and
that depends on what those things are.
And the Visual Image Store is very, very brief.
It decays in about 200 milliseconds.
And this 200 milliseconds is our persistence of the vision.
It's the fact that something can be flashed to our eye.
And our eye remembers what it sees for about two tenths of a second.
And that's enough time for the perceptual processor to catch it.
It's also enough time to do certain processes like reading.
And so when we read it may appear, when you read the text on the slide,
that your eye is moving forward in a very continuous fashion.
Because it's how we're processing the information.
And it may seem like we are processing every letter at a time, but
that's not what's happening.
What happens is we have this Saccades and our eyes jerking from one location to
another, and we do those very quickly, and the our eye fixates on a single portion.
And we look at that portion and the surrounding images on our retina.
And we don't perceive our Saccades.
Our eye is jerking left and right, and we don't see that.
Our perceptual system blanks that out from our conscience selves.
In terms of reading, we can read smaller fonts and
larger fonts about at the same rate.
Although larger fonts we probably feel are easier to read,
we read them at the same rate.
And we still tend to read printed pages faster than we read displays.
This text on the right shows that you're not processing every letter, but
you're processing letters in chunks, and
we can still read this even though it's doesn't itself itself really make sense.
There's also audio processing, and I haven't drawn the ear yet.
But you have an ear that sends signals to an Auditory Image Store.
And again, about five letters of information, five things of information.
And when you hear something, you have about a minute and a half to process it.
So if you're not paying attention to something, if somebody's talking to you
and you're not paying attention, and they say, what did I just say?
If they said it in the last one and a half seconds, then you have a pretty good
chance of being able to recover it, without even thinking about it.
And so the way that the ear works, there is an auditory
canal in your ear that focuses sounds that vibrates in eardrum, that basically
vibrates some mechanical structures that then works its way around the cochlea.
That's basically figuring out what frequency composes the sound and
stimulates the cilia that then sends signals to the rest of your
perceptual system.
And the sound, there's Pitch, Loudness, and Timbre.
Pitch is the frequency, where in the cochlea it gets sensed.
Loudness is the amplitude of the waveform.
And then timbre is the shape of the waveform, and that can differ based on for
example, different instruments produce different timbres of sound.
We can do stereo location of a source.
We have two ears, and we can do some stereo location of a source, but
in general it's not very accurate.
The more important thing is we have a cocktail party effect that means that
we can focus on a particular conversation and
mentally filter out some of the other noise that's going on and
humans still much better than computers ever have and able to.
And finally, Hand-Eye Coordination.
And this pathway's a little bit more involved.
We have an eye, a Visual Image Store,
that's helping us process the imagery from the eye.
Then a Perceptual Processor that's actually doing the processing on the data
in this Visual Image Store.
That gets sent to the brain that decides what to do with it.
And then the Motor Processor, the hand.
So we've got this Hand-Eye Coordination that goes through all these paths.
And you can start adding up the time, 230 milliseconds for the eye.
Another 100 milliseconds for you to perceive what the eye is seeing.
Another 70 seconds for you to think about the eye.
Then another 70 seconds for your hand to respond to instructions from your brain.
And so you can use that to figure out how long it takes for
you to react, or how long it takes for you to move the mouse.
If I have the mouse in some location, and I want to move the mouse say to
this figure's head, I can figure out how long it's going to take me to do that.
And this is based on several properties of the human kinesthesis,
the fact that we know where our limbs are.
We know where our fingers bringing things to.
And also the fact that larger movements are faster.
I can move the mouse quickly across the screen and click as well.
I can move the mouse quickly across the screen, but I do it less accurately.
And so we tend to, when we're finding a target like if I want to find the center
of the zero and that 70 milliseconds, I move quickly towards the 0 but
then I have to do a series of finer and finer steps.
So we do large lunges followed by finer and finer refinements.
And so that gives us a logarithmic rate at which we find things.
And so, if I need to move things farther than the distance, D gets larger.
But if I need to get to a smaller object, then the size matters as well.
So I need to divide by the size, larger things.
If I need to point to a larger thing, it should take me less time, so
it's one over the size.
So the distance divided by the size gives me an indication I can point to the larger
things farther away as fast as I can point to smaller things closer to me.
And we take the logarithm because the number of steps I need is based on this
distance over size, I'm taking shorter and shorter steps.
So this is a log base 2.
And then we have 240 milliseconds it takes for each one of those lunges.
240 milliseconds is 70 milliseconds to move your hand,
takes you 100 milliseconds to be able to see the result of your lunge.
And then another 70 milliseconds to decide how to correct it with your next lunge.
So 70 milliseconds to move the hand, 100 milliseconds to see the result, and
then another 70 milliseconds to figure out what the next Instruction to your hand
ought to be.
That's 240 millisecond on average.
And then you need about 600 milliseconds, six-tenths of a step,
just to prepare yourself to be able to do this exercise.
So this is a rough approximation called Fitt's Law that tells you how quickly it
should take you to move the mouse to a certain region of
the screen based on where the mouse is to begin with.
So what did we learn?
We learned the model human processor,
which describes the human in terms of a computer.
Which might be more familiar to some computer science students.
And we can use that model to predict how a human will perceive data,
and react to data.
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