1. Data Mining in Micro array Analysis
Classification (Supervised Learning)
Finding models (functions) that describe and distinguish classes or concepts for future prediction
E.g., predict disease based on gene expression profiles
Similar to Prediction: Predict some unknown or missing categorical value rather than a numerical values
Presentation: decision-tree, classification rule, neural network
Cluster analysis (Unsupervised Learning)
Class label is unknown: Group data to form new classes, e.g., cluster genes to find distribution patterns
Clustering based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity
E.g. Group genes based on their gene expression profiles

2. Supervised vs Unsupervised Learning
Classification
Unsupervised
Clustering
unknown number of classes
known number of classes
based on a training set
no prior knowledge
used to classify future observations
Als dritte Methode werde ich hier etwas über Pattern recognition erzählen. Bei dieser Methode beschäftigt man sich mit Entscheidungsfindungsprozessen. Diese Prozesse will man zuerst verstehen um sie dann mithilfe von Computern zu automatisieren.
Die Methode des Pattern recognitions lässt sich in die 2 Klassen supervised und unsupervised unterteilen. In der Kategorie der supervised pattern recognition geht man von einer bekannten Anzahl Klassen aus. Bei der unsupervised PR ist die Anzahl Klassen unbekannt.
Supervised PR basiert auf einem sogenannten training set. Dies ist eine Reihe von Beobachtungen, bei denen man die Einteilung in die Klassen bereits kennt. Aufgrund dieser vorher bekannten Zuordnung werden die eigentlichen Beobachtungen mit unbekannter Klasseneinteilung den Klassen zugeordnet. In der Variante der unsupervised PR geht man von keinem a priori Wissen aus.
Für die Klassierung von zukünftigen Beobachtungen in vorgegebene Klassen wird supervised PR verwendet. Die Clusteranalyse, wie sie gerade vorgestellt wurde, gilt als eine Form der unsupervised PR.
Somit möchte ich nicht weiter auf unsupervised PR eingehen. Im folgenden soll die Form der supervised PR vorgestellt werden.
used to understand (explore) data

3. Supervised vs. Unsupervised Learning* o
income
debt
debt + + + + + + + + + + + + + + + + + + + + +
Supervised
Learning
Unsupervised
Learning +
debt * o
income
debt
income

4. Classification Training Set Data with unknown classes
Data with known classes
Data with unknown classes
Class
Assignment
Classification
Technique
Classifier

5. Types of Classifiers * income debt Linear Classifier:
Non Linear Classifier: * o
income
debt
debt * * * o o o * * o o * * o * * o * o * o o
income
a*income + b*debt < t => No loan !

6. Predictive Modelling:
Day Outlook Temperature Humidity Wind Play Tennis
1 Sunny Hot High Weak No
2 Sunny Hot High Strong No
3 Overcast Hot High Weak Yes
4 Rain Mild High Weak Yes
5 Rain Cool Normal Weak Yes
6 Rain Cool Normal Strong No
7 Overcast Cool Normal Strong Yes
8 Sunny Mild High Weak No
9 Sunny Cool Normal Weak Yes
10 Rain Mild Normal Weak Yes
11 Sunny Mild Normal Strong Yes
12 Overcast Mild High Strong Yes
13 Overcast Hot Normal Weak Yes
14 Rain Mild High Strong No
Predict categorical class labels
Classify data (construct a model) based on the training set and the values (class labels) in a classifying attribute and
Use it in classifying new data

7. Classification Learning : Induce classifiers from training data
Task: determine which of a fixed set of classes an example belongs to
Input: training set of examples annotated with class values.
Output:induced hypotheses (model/concept description/classifiers)
Learning : Induce classifiers from training data
Inductive
Learning
System
Training
Data:
Classifiers
(Derived Hypotheses)
Predication : Using Hypothesis for Prediction: classifying any example described in the same manner
Classifier
Decision on class
assignment
Data to be classified

8. Decision Tree: Example
Day Outlook Temperature Humidity Wind Play Tennis
1 Sunny Hot High Weak No
2 Sunny Hot High Strong No
3 Overcast Hot High Weak Yes
4 Rain Mild High Weak Yes
5 Rain Cool Normal Weak Yes
6 Rain Cool Normal Strong No
7 Overcast Cool Normal Strong Yes
8 Sunny Mild High Weak No
9 Sunny Cool Normal Weak Yes
10 Rain Mild Normal Weak Yes
11 Sunny Mild Normal Strong Yes
12 Overcast Mild High Strong Yes
13 Overcast Hot Normal Weak Yes
14 Rain Mild High Strong No
Outlook
Sunny
Overcast
Rain
Humidity
Yes
Wind
High
Normal No
Strong
Weak

9. Classification: Relevant Gene Identification
Goal: Identify subset of genes that distinguish between treatments, tissues, etc.
Method
Collect several samples grouped by treatments (e.g. Diseased vs. Healthy)
Use genes as “features”
Build a classifier to distinguish treatments

10. Gene Expression Example
ID G1 G2 G3 G4 Cancer No No
Yes
Yes
Yes No
Yes
Yes
Yes
Yes
Yes No
Yes No
15 ….. … G1
>22 G3 G4
<=12
>12 No
Yes
<=52
>52
<=22
Problem: With large number of genes (~10000)
Need to use feature selection/reduction techniques