Download Data Mining by Johan Johansson

Data Mining
By: Johan Johansson
Mining Techniques
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Association Rules
Decision Trees
Clustering
Nearest Neighbor
Neural Networks
Association Rules
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If the confidence for Bread -> Milk is 80% then
it does NOT imply the Milk -> Bread has a
confidence of 80%.
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A good rule has both high confidence and high
support.
Decision Trees
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Produces rules that are mutually exclusive as
opposed to Association rules where there might
be a lot of redundancy.
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Rule induction goes from the bottom up and
collect all possible patterns that are interesting.
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Decision trees do a greedy search, looking for
the best possible split on the next step.
K-Nearest Neighbors
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Instead of selecting the nearest neighbor, let’s
take a “vote” from the K nearest neighbors.
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Larger values of K reduce the effect of noise on
the classification, but make boundaries between
classes less distinct.
K-Nearest Neighbors
Main differences between Clustering
and Nearest Neighbor
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Clustering is mostly used for consolidation while
Nearest Neighbor is also used for predictions.
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Nearest Neighbor uses supervised learning,
which is defined by the problem so it can make
accurate and relevant predictions.
Neural Networks
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Mimic the structure and functioning of the
human brain.
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Using sophisticated pattern detection and
machine learning algorithms.
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Hard to use and hard to implement but very
effective.
Simple Neural Network
Simple Neural Network (cont’d)
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Age and income (nodes) is based on fuzzy logic,
where is the values are between 0 and 1,
inclusive.
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The weights (links) determines the output,
which is between 0 and 1, inclusive.
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0.47(0.7) + 0.65(0.1) = 0.39 which is closer to 0
than 1 so it’s not the “default” in this case.
References
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http://www.thearling.com/text/dmtechniques/
dmtechniques.htm
http://en.wikipedia.org/wiki/Data_mining