Data Mining:

Knowledge Discovery in Databases

Peter van der Putten

ALP Group, LIACS

Pre-University College

LAPP-Top Computer Science

February 2005

Topics

• Lecture• Demo Data Mining tool• Exercises Data Mining tool• Breaks TBD

Data mining case studyDARPA’s Bio-surveillance Agent

Data mining case studyDARPA’s Bio-surveillance Agent

All applications

Expert knowledge 29.8% accepted

12.7% infection

34.5% accepted

Prediction model plus rules

9.1% infection

Accepted Accepted volumevolume

Data mining case study Credit Scoring for Loan Acceptance

© Chordiant Software

Data mining case study Credit Scoring for Loan Acceptance

© Chordiant Software

Data mining case studyClassifying Leukemia

• Problem:– Leukemia (different types of Leukemia cells look very

similar)– Given data for a number of samples (patients), can

we• Accurately diagnose the disease? • Predict outcome for given treatment?• Recommend best treatment?

• Solution– Data mining on micro-array data

Data mining case studyClassifying Leukemia

• 38 training patients, 34 test patients, ~ 7,000 patient attributes (microarry gene data)

• 2 Classes: Acute Lymphoblastic Leukemia (ALL) vs Acute Myeloid Leukemia (AML)

• Use train data to build diagnostic model

ALL AML

• Results on test data: 33/34 correct, 1 error may be mislabeled

5 million terabytes created in 2002

• UC Berkeley 2003 estimate: 5 exabytes (5 million terabytes) of new data was created in 2002.

• Twice as much information was created in 2002 as in 1999 (~30% growth rate)

• Other growth rate estimates even higher• Very little data will ever be looked at by a human• Knowledge Discovery is NEEDED to make

sense and use of data.

Dilbert puts data mining in perspective

Sources of (artificial) intelligence

• Reasoning versus learning• Learning from data

– Patient data– Customer records– Stock prices– Piano music– Criminal mugshots– Websites– Robot perceptions– Etc.

Some working definitions….

• ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably

• Data mining = – The process of discovery of interesting, meaningful and

actionable patterns hidden in large amounts of data • Multidisciplinary field originating from artificial

intelligence, pattern recognition, statistics, machine learning, bioinformatics, econometrics, ….

Some working definitions….

• Concepts: kinds of things that can be learned– Aim: intelligible and operational concept description– Example: the relation between patient characteristics

and the probability to be diabetic• Instances: the individual, independent examples of a

concept– Example: a patient, candidate drug etc.

• Attributes: measuring aspects of an instance– Example: age, weight, lab tests, microarray data etc

• Pattern or attribute space

Data mining tasks

• Predictive data mining– Classification: classify an instance into a category– Regression: estimate some continuous value

• Descriptive data mining– Matching & search: finding instances similar to x– Clustering: discovering groups of similar instances– Association rule extraction: if a & b then c– Summarization: summarizing group descriptions– Link detection: finding relationships– …

Data Mining Tasks: Search

f.e. age

f.e.

wei

ght

Finding best matching instances

Every instance is a point in pattern space. Attributes are the dimension of an instance, f.e. Age, weight, gender etc.

Pattern spaces may be high dimensional (10 to thousands of dimensions)

Data Mining Tasks: Classification

age

weig

ht

Goal classifier is to seperate classes on the basis of known attributes

The classifier can be applied to an instance with unknow class

For instance, classes are healthy (circle) and sick (square); attributes are age and weight

Data Mining Tasks: Clustering

f.e. age

f.e.

wei

ght

Clustering is the discovery of groups in a set of instances

Groups are different, instances in a group are similar

In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user

Data Mining Tasks: Clustering

f.e. age

f.e.

wei

ght

Clustering is the discovery of groups in a set of instances

Groups are different, instances in a group are similar

In 2 to 3 dimensional pattern space you could just visualise the data and leave the recognition to a human end user

In >3 dimensions this is not possible

Examples of Classification Techniques

• Majority class vote• Machine learning & AI• Decision trees• Nearest neighbor• Neural networks• Genetic algorithms / evolutionairy computing• Artificial Immune Systems• Good old statistics• …..

Example Classification Algorithm 1Decision Trees

20000 patients

age > 67

18800 patientsgender = male?

1200 patientsWeight > 85kg

800 customersDiabetic (%10) etc.400 patients

Diabetic (%50)

no

noyes

yes

no

Decision Trees in Pattern Space

age

weig

ht

Goal classifier is to seperate classes (circle, square) on the basis of attribute age and income

Each line corresponds to a split in the tree

Decision areas are ‘tiles’ in pattern space

Decision Trees in Pattern Space

age

wei

ght

Goal classifier is to seperate classes (circle, square) on the basis of attribute age and income

Each line corresponds to a split in the tree

Decision areas are ‘tiles’ in pattern space

Special Cases of Decision Trees

• Depth = 0– Majority class classifier (ZeroR)

• Depth = 1– One question only– Also known as decision stump

• Depth = n– Any amount of branches

• Various algorithms exist to learn the tree from data– Major difference is criterion to determine on what attribute

value to split

Example classification algorithm 2:Nearest Neighbour

• Data itself is the classification model, so no abstraction like a tree etc.

• For a given instance x, search the k instances that are most similar to x

• Classify x as the most occurring class for the k most similar instances

= new instance

Any decision area possible

Condition: enough data available

Nearest Neighbor in Pattern Space

Classification

fe age

fe w

eigh

t

Nearest Neighbor in Pattern Space

Voorspellen

f.e. age

bvb.

wei

ght

Any decision area possible

Condition: enough data available

Example classification algorithm 3:Neural Networks

• Inspired by neuronal computation in the brain (McCullough & Pitts 1943 (!))

• Input (attributes) is coded as activation on the input layer neurons, activation feeds forward through network of weighted links between neurons and causes activations on the output neurons (for instance diabetic yes/no)

• Algorithm learns to find optimal weight using the training instances and a general learning rule.

invoer:bvb. klantkenmerken

uitvoer:bvb. respons

• Example simple network (2 layers)

• Probability of being diabetic = f (age * weightage + body mass index * weightbody mass index)

Neural Networks

Weightbody mass index

Probability of being diabetic

age body_mass_index

weightage

Neural Networks in Pattern Space

Classification

f.e. age

f.e.

wei

ght

Simpel network: only a line available (why?) to seperate classes

Multilayer network:

Any classification boundary possible

e Decision Tree Demo in WEKA,An open source mining tool

Descriptive data mining:association rules

• Discovery of interesting patters• Rule format: if A (and B and C etc) then Z• Example:

– If customer buys potatoes (A) and sauerkraut (B) then customer buys sausage (Z)

• Quality measures for a rule– Support condition: how often do potatoes and sauerkraut

occur together (A,B)– Confidence rule: how often do sausages then occur / support

conditions (is A,B C always true?)

e Associatie rule demo in WEKA

Some examples of my research areas(Jointly with students)

• Mix between applications and new algorithms– Video mining: recognize settings, porn filtering– Artificial Immune Systems: copying learning ability of immune

systems– Predicting Survival Rate for Throat Cancer Patients– Crime Data Mining– Fusing Data from Multiple Sources– Decisioning: offering the right product to the right customer

using predictions– Bias variance evaluation: distinguish between different

sources of error for a classifier

What have we learned so far?

• Case Studies• Learning versus reasoning• Data mining definitions• Data mining tasks• Example data mining techniques for classification• Example data mining techniques for association rules• WEKA Demos• And now: lab sessions