Transcript Machine Learning, Data Mining ISYS370 Dr. R. Weber

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Machine Learning, Data Mining
ISYS370
Dr. R. Weber
The game
• How did you reason to find the rule?
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• According to Michalski (1983) A theory and
methodology of inductive learning. In Machine
Learning, chapter 4,
“inductive learning is a heuristic search through a
space of symbolic descriptions (i.e., generalizations)
generated by the application of rules to training
instances.”
Learning
• Rote Learning
– Learn multiplication tables
• Supervised Learning
– Examples are used to help a program identify a concept
– Examples are typically represented with attribute-value pairs
– Notion of supervision originates from guidance from
examples
• Unsupervised Learning
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– Human efforts at scientific discovery, theory formation
Inductive Learning
• Learning by generalization
• Performance of classification tasks
– Also categorization
• Rules indicate categories
• Goal:
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– Characterize a concept
Concept Learning is a Form of
Inductive Learning
•Learner uses:
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–positive examples (instances ARE examples of a
concept) and
–negative examples (instances ARE NOT examples of
a concept)
Concept Learning
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• Needs empirical validation
• Dense or sparse data determine quality of
different methods
Validation of Concept Learning i
• The learned concept should be able to correctly
classify new instances of the concept
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– When it succeeds in a real instance of the concept it
finds true positives
– When it fails in a real instance of the concept it
finds false negatives
Validation of Concept Learning ii
• The learned concept should be able to correctly
classify new instances of the concept
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– When it succeeds in a counterexample it finds true
negatives
– When it fails in a counterexample it finds false
positives
Rule Learning
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• Learning widely used in data mining
• Version Space Learning is a search method to
learn rules
• Decision Trees
Version Space ii
A=1
B=1
B=.5
C=1
C=1
C=.5 C=.3
C=.3
C=.5
B=0
C=1
C=.3
C=.5
Outcome ?Outcome ?Outcome ?Outcome ?Outcome ?Outcome ?Outcome ?
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• Creates tree that includes all possible combinations
• Does not learn for rules with disjunctions
• Incremental method, trains additional data without the need to
retrain all data
Decision trees
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• Knowledge representation formalism
• Represent mutually exclusive rules (disjunction)
• A way of breaking up a data set into classes or
categories
• Classification rules that determine, for each
instance with attribute values, whether it
belongs to one or another class
• Not incremental
Decision trees
consist of:
-leaf nodes (classes)
- decision nodes
(tests on attribute values)
-from decision nodes
branches grow for each
possible outcome of the test
From Cawsey, 1997
Decision tree induction
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• Goal is to correctly classify all example data
• Several algorithms to induce decision trees:
ID3 (Quinlan 1979) , CLS, ACLS,
ASSISTANT, IND, C4.5
• Constructs decision tree from past data
• Attempts to find the simplest tree (not
guaranteed because it is based on heuristics)
ID3 algorithm
•From:
– a set of target classes
–Training data containing objects of more than one
class
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•ID3 uses test to refine the training data set into
subsets that contain objects of only one class
each
•Choosing the right test is the key
How does ID3 chooses tests
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• Information gain or ‘minimum entropy’
• Maximizing information gain corresponds to
minimizing entropy
•Predictive features (good indicators of the
outcome)
Choosing tests
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• Information gain or ‘minimum entropy’
• Maximizing information gain corresponds to
minimizing entropy
•Predictive features (good indicators of the
outcome)
Clustering
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Data analysis method applied to data
Data should naturally possess groupings
Goal: group data into clusters
Resulting clusters are collections where objects within a cluster
are similar to each other
• Objects outside the cluster are dissimilar to objects inside
• Objects from one cluster are dissimilar to objects in other
clusters
• Distance measures are used to compute similarity
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• http://movielens.umn.edu/main
Explanation-based
learning
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• Incorporates domain knowledge into the
learning process
• Feature values are assigned a relevance factor
if their values are consistent with domain
knowledge
• Features that are assigned relevance factors
are considered in the learning process
Familiar Learning Task
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Learn relative importance of features
Goal: learn individual weights
Commonly used in case-based reasoning
Methods include a similarity measure to get feedback
about verify their relative importance: feedback
methods
• Search methods: gradient descent
• ID3
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Esteem CBR shell has
option to use Gradient Descent
to learn weights:
Data mining tasks ii
• Link analysis
Rules:
• Association generation
• Relationships between entities
• Deviation detection
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• How things change over time, trends
KDD applications
• Fraud detection
– Telecom (calling cards, cell phones)
– Credit cards
– Health insurance
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Loan approval
Investment analysis
Marketing and sales data analysis
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Identify potential customers
Effectiveness of sales campaign
Store layout
Text mining
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The problem starts with a query and
the solution is a set of information
(e.g., patterns, connections, profiles,
trends) contained in several different
texts that are potentially relevant to
the initial query.
Text mining applications
• IBM Text Navigator
– Cluster documents by content;
– Each document is annotated by the 2 most frequently
used words in the cluster;
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• Concept Extraction (Los Alamos)
– Text analysis of medical records;
– Uses a clustering approach based on trigram
representation;
– Documents in vectors, cosine for comparison;
Problem-solving
method
rule-based ES
Reasoning
type
deductive reasoning
case-based reasoning
analogical reasoning
inductive ML, NN
inductive reasoning
algorithms
search