Transcript - A Powerful Computing Technology Department of Computer Science Wayne State University.

- A Powerful Computing Technology
Department of Computer Science
Wayne State University
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Road Map
 Overview
 Recommender Systems
 Clustering
 Classification
 Association Analysis
 PageRank
 Social Networks
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Different Forms of Data
 Text Data
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Different Forms of Data
 Image Data
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Different Forms of Data
 Video Data
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Different Forms of Data
 Network Data
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Why Data Mining is Important?
 Difficulty of identifying patterns in big data.
 Extracting only WANTED data within a short time.
We are drowning in data, but starving for knowledge!
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How Data Mining can help?
 We do not care if GOOGLE has more than billion
web pages.
 We only care about the information that is useful
for us.
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What is Data Mining
 The analysis of data to extract useful patterns or
information from a large data collection.
Automated Analysis of Massive Data
 Also known as: Knowledge Discovery in Databases
 Learn More: http://en.wikipedia.org/wiki/Data_mining
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Applications
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Data Miner
 An educational tool that teaches you Data Mining
techniques.
 Consists of two basic parts such that,
 Demonstration

Explains how to work with the interactive part.
 Interactive part

Teaching data mining through user interaction.
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Recommender Systems
 Goal:
present information items that are likely to be of interest to
the user.
 Lots of online products, books, movies, etc.
 Reduce my choices…please!!!!
 Learn More: http://pespmc1.vub.ac.be/collfilt.html
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Recommender Systems
 Netflix Recommender System
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Do you watch movies using
Then you
might like
If you have
watched
this movie
Or may be
you like
So on you might
like these too
This might catch
your interest too
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Amazon Recommender System
 Amazon Recommender System
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Data Miner - Recommender System
 Recommendation based on content
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Recommendation
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Finding a Friend With Similar Taste
YOU
See what they like
Measure the similarity
Select your Neighbors
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Measuring the Similarity
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Cluster Analysis
 Cluster:
 A collection of data objects
 Cluster Analysis:
 Grouping some given objects with similar attributes.
 Similar (or related) to one another within the same group
 Dissimilar (or unrelated) to the objects in other groups
 Learn More: http://home.dei.polimi.it/matteucc/Clustering/tutorial_html
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Cluster Analysis
 Data Set:
 Clusters:
Flowers
Fruit
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Clustering
 Now you have seen Flowers and Fruits visually.
Flowers
Fruit
 So to which cluster, would you add this object?
Yes, to FRUIT!!
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Classification
 Assigning given items to a known class which have items
with similar attributes.
 Explains through Decision Trees.
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Classification
 PURE Classification.
 Each branch contains animals belong to a single CLASS.
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Classification
 You have learned what is Mammal and what is Bird.
 Can you tell what is this?
Yes, this is indeed a BIRD!!
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Association Analysis
 Discover interesting relationships in a set of transactions.
 Understand relationships between items.
E.g.
 If a customers buys shoes, then 10% of the times they also buys socks.
 60% of all shoppers will buy bread when they also purchase a pint of
milk.
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Association Analysis
 Items:
 Transactions:
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PageRank
 Links from popular and related web sites increases the popularity of the given
web site.
Amazon
Yahoo
Pillsbury
YouTube
Billboard
Pandora
Dominos
Pizza
Crayola
Pizza
Hut
Danskin
Shelfari
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Search Results
 When searching on Google, it
will list web sites related to the
input text according to their
importance.
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Social Networks
 Social networking websites allow users to be part of a
virtual community.
E.g. Facebook, Twitter, MySpace
 They provide users with simple tools to create a
custom profile with text and pictures.
 Users can share their lives with other people through
these networks.
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Social Networks
 Learn More:
 http://en.wikipedia.org/wiki/Social_network
 http://pc.net/glossary/definition/socialnetworking
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Thank You !!
Enjoy the Day…
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