A Pattern is a
repeated decorative design. We, humans
are very good recognizing Patterns in our every day life. We
can't live without patterns. Our
brain is actually classifying things based patterns and everyday we are
learning patterns only. There is a huge difference between what we
study and what we practice. Even when
there were no colleges, humans should
distinguish between friend and foe using patterns.
For example, if I
show you the following diagram, what you will say
Most Probably (due
to my poor drawing), you will say it is a car.
But is it resembling any car parked outside. What we recognize a car is nothing but the
shape, wheels, window, steering wheel and head light. Rest of the details are unimportant to us.
Let us take an
example of a 6 months old toddler. He
cannot talk, he cannot move and he cannot understand most things. But still he can immediately smiles at his
mother. The baby can do a pattern
recognition in no time. Because it is an
essential part of our living.
Recognizing mother is the first important thing for our survival.
There are lot of
ways in which patterns can be found.
They are classified into two broad categories, Supervised and
Unsupervised. Recognizing mother is
unsupervised. The baby forms clusters of
people who are moving with him frequently and all other people are placed in a
different cluster. But recognizing car
is a supervised learning. Someone needs
to tell the child that the picture is a car, what Papa is driving is a car
etc. Upon repeatedly interacting and
experiencing the child learns that it is a car.
There are many
algorithms available to find the pattern.
Ok, after finding the pattern, what one is going to do. The pattern can be used to do predictive
analysis. For eg, I collect all the data
of school kids in US and analyze them. I
will also check where each kid ended up.
By figuring out the correlation (we will come back to this later)
between the data and where the kid ended up, I can draw a conclusion that what
is the pattern of the school drop outs.
When I get current
data, say the end of first semester data, by applying the above pattern, I can
figure out which kids are likely to be drop out of school. I can take effective actions to address the
problems of that particular kid and save him/her.
The above is an
example of Supervised learning. I have
the past data with grades and where the kids ended. I am finding a pattern, in other words, a
mathematical formula to approximate my findings within a reasonable error
limit. When a new data comes in, I can
check what the output is going to be.
This technique is
used in many many areas, such as credit card ratings, fraud detection, Spam
filtering etc.
If I don't know the
output, I can organize objects into groups whose members are similar in some
way. For eg, I am observing some
symptoms (headache, temperature etc.) I want to check whether the person has flu or
brain tumor. Even though all the
symptoms of any disease will be present in all the patients, still I can
classify the people into one of the groups such as having flu, having brain
tumor etc.
There are many
interesting algorithms to work with Supervised and Unsupervised. I will continue the articles explaining them
more.
