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Lecture 5: Automatic cluster detection

Lecture 6: Artificial neural networks

Lecture 7: Evaluation of discovered knowledge

Brief introduction to lectures

Transparencies prepared by Ho Tu Bao [JAIST]

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Lecture 5: Automatic Cluster Detection

•One of the most widely used KDD classification techniques for unsupervised data.

•Content of the lecture

1. Introduction2. Partitioning Clustering3. Hierarchical Clustering4. Software and case-studies

•Prerequisite: Nothing special

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Partitioning Clustering

:conditions following the satisfying ,clusters called often n),(K

X of P,...,P,P subsets empty-non disjointK of collection a is

}x,...,x,{xX objects n of set a of partition A

K21

n21

• Each cluster must contain at least one object• Each object must belong to exactly one group

P of components called are P },P,...,P,{PP partition the Denote iK21

X.P...PP:X is union their (2)

ji ,P and P all for 0PP :disjoint are they (1)

K21

jiji

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Partitioning ClusteringWhat is a “good” partitioning clustering?

Key ideas: Objects in each group are similar and objects between different groups are dissimilar.

Minimize the within-group distance and Maximize the between-group distance.

Notice: Many ways to define the “within-group distance” (the average of distance to the group’s center or the average of distance between all pairs of objects, etc.) and to define the “between-group distance”. It is in general impossible to find the optimal clustering.

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Hierarchical Clustering

A hierarchical clustering is a sequence of partitions in which each partition is nested into the next partition in the sequence.

Partition Q is nested into partition P if every component of Q is a subset of a component of P.

(This definition is for bottom-up hierarchical clustering. In case of top-down hierarchical clustering, “next” becomes “previous”).

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Bottom-up Hierarchical Clustering

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Top-Down Hierarchical Clustering

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OSHAM: Hybrid Model

WisconsinBreastCancerData

Attributes

Brief Descriptionof Concepts

ConceptHierarchy

Multiple Inheritance Concepts

Discovered Concepts

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Lecture 1: Overview of KDD

Lecture 2: Preparing data

Lecture 3: Decision tree induction

Lecture 4: Mining association rules

Lecture 5: Automatic cluster detection

Lecture 6: Artificial neural networks

Lecture 7: Evaluation of discovered knowledge

Brief introduction to lectures

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Lecture 6: Neural networks

•One of the most widely used KDD classification techniques.

•Content of the lecture

•Prerequisite: Nothing special

1. Neural network representation2. Feed-forward neural networks3. Using back-propagation algorithm4. Case-studies

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Lecture 1: Overview of KDD

Lecture 2: Preparing data

Lecture 3: Decision tree induction

Lecture 4: Mining association rules

Lecture 5: Automatic cluster detection

Lecture 6: Artificial neural networks

Lecture 7: Evaluation of discovered knowledge

Brief introduction to lectures

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Lecture 7 Evaluation of discovered knowledge

•One of the most widely used KDD classification techniques.

•Content of the lecture

1. Cross validation2. Bootstrapping3. Case-studies

•Prerequisite: Nothing special

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Out-of-sample testing

HistoricalData

(warehouse)Samplingmethod

Sampledata

Samplingmethod

Trainingdata

Inductionmethod

Testingdata

Errorestimation

Model

2/3

1/3

error

The quality of the test sample estimate is dependent on the number of test cases and the validity of the independent assumption

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Cross Validation

HistoricalData

(warehouse)Samplingmethod

Sampledata

Samplingmethod

Sample 1 Inductionmethod

Sample nError

estimation

Model

Run’serror

10-fold cross validation appears adequate (n = 10)

Sample 2

...

Errorestimation

iterate

- Mutually exclusive- Equal size

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randomly split the data set into 3 subsets of equal size

run on each 2 subsets as training

data to find knowledge

test on the rest subset as testing data to evaluate the accuracy

average theaccuracies asfinal evaluation

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3

1

1

2

2A data set

A method to be evaluated

Evaluation: k-fold cross validation (k=3)

1 3

3 2

3 1

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Outline of the presentation

Objectives,

Prerequisite

and Content

Brief

Introduction

to Lectures

Discussion

and

Conclusion

This presentation summarizes the content and organizationof lectures in module “Knowledge Discovery and Data Mining”