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Data Mining and Machine Learning. Addison Euhus and Dan Weinberg. Introduction. Data Mining: Practical Machine Learning Tools and Techniques (Witten, Frank, Hall) What is Data Mining/Machine Learning? “Relationship Finder” Sorts through data… computer based for speed and efficiency

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data mining and machine learning

Data Mining and Machine Learning

Addison Euhusand Dan Weinberg

introduction
Introduction
  • Data Mining: Practical Machine Learning Tools and Techniques (Witten, Frank, Hall)
  • What is Data Mining/Machine Learning? “Relationship Finder”
  • Sorts through data… computer based

for speed and efficiency

  • Weka Freeware
  • Applications
components of machine learning
Components of Machine Learning
  • Concept/Relation: What is being learned from the data set
  • Instances: “Input”
  • Attributes: “Values”
    • Nominal, Ordinal, Interval, Ratio
    • “Name, Order, Distance, Number”
the output
The Output
  • Variety of forms depending on the concept to be learned
  • Can be a table, regression, chart/diagram, grouping/clustering, decision tree, or set of rules
  • Different output will describe different things about same set of data
  • Cross Validation
some algorithms in machine learning
Some Algorithms in Machine Learning
  • Decision Trees (ID3 Method)
    • Split an attribute into tree nodes and categorize the data and continue splitting into further nodes
    • Want to chose the attribute that is most “pure” – quantitatively speaking, most amount of “information”
    • Entropy(x,y,z) = -xlog2(x)-ylog2(y)-zlog2(z), the amount of uncertainty in the data set
    • info([a,b,c]) = entropy(a/t,b/t,c/t) where t=a+b+c
decision trees continued
Decision Trees Continued
  • Gain(Attribute) = info(unsplit) – info(split)
  • The attribute with the most gain should be split first in the decision tree algorithm
  • An example:

Gain = info([9,5])-info([2,3],[4,0],[3,2]) = .247

Attribute

Yes

Yes

Yes

No

No

Yes

Yes

No

No

No

Yes, Yes, Yes, Yes

other algorithms
Other Algorithms
  • Covering Approach:
    • Creates a set of rules, unlike a decision tree
    • However, Same top-down, divide and conquer approach
    • Begin with the end values and then choose the attribute with the most “positive instances”
  • For ratio attributes, models such as line-of-best-fit and other regressions are used
  • Least-Squares regression
instance based learning
Instance-Based Learning
  • At run-time the program uses a variety of different calculations to find instances that are most similar to the test instance
  • Find the “nearest-neighbor,” based off of “distances”
  • Measures “distances” and how close one instance is to another. The closest instances have their value assigned to the test instance
  • Standard Euclidean or alternative forms
applications
Applications
  • Wine Quality Dataset
  • Nearest Neighbor: 65.39% Accuracy
  • Decision Tree: 58.25% Accuracy
wine quality output
Wine Quality Output

Decision Tree

Nearest Neighbor

a challenging classification problem
A Challenging Classification Problem
  • Shortfalls and Limitations
    • Poker Hands: (Used with over 5,000 different instances)
    • Decision Tree: 55.90%
    • Nearest Neighbor: 46.86%
    • Some speed algorithms: <35%

The issue: Suit is numerical(0,1,2,3) as well as the orderthe cards are presented in