Data Mining - Overviericher/dm/data_mining_1_overview.pdf · Deﬁnition 1 Data Mining (Minería de...

Data Mining - Overview

Dr. Jean-Michel RICHER

[email protected]

Dr. Jean-Michel RICHER Data Mining - Overview 1 / 27

Outline

1. Introduction

2. What will we cover ?

3. Skills required and to acquire

4. Evaluation


1. Introduction


Subject

Definition 1Data Mining (Minería de Datos) is the extraction of im-plicit, previously unknown, and potentially useful informa-tion from data.

Definition 2Data Mining is the computing process of discovering pat-terns in large data sets involving methods at the intersec-tion of machine learning, statistics, and database systems.


What do they have in common ?

Common termsdiscovery, extraction (not obvious)useful information, patterns, rules (form of the usefulinformation)data, database (potentially a huge amount of data)statistics, machine learning, ... (methods)


What is data mining ?

DefinitionData Mining is a branch of computer science which is ei-ther

a knowledge discovery process of hidden or nontrivial information (ex: supermarket rules)or a knowledge synthesis process used to improve theunderstanding of raw data (ex: flower classification)


Data definition

More generally

given a set of Objects X = { ~X1, . . . ,~Xn} (persons,

customers, patients, plants, ...)defined by a set of properties P = {P1, . . . ,Pk} (length,size, weight, color, ...)each Pi having discrete or continuous valueswe want to find

I patterns / rules than can explain the relationshipsbetween objects and their properties

I classify objects


What are properties ?

A note on propertiesProperties can be

quantitative: a numerical valuequalitative: a text

ExampleFor temperatures:

quantitative and continuous: [−20◦C,+40◦C]

qualitative and discrete: very cold, cold, ..., hot, veryhot


From data to knowledge

Objects P1 P2 ... Pk

~X1 x11 x2

1 ... xk1

...~Xn x1

n x2n ... xk

n

if (x1i > 10) and (x3

i == true) then x7i = humid

x1i = 1.23× x2

i + 2.56x3i

c1 = { ~X1,~X3},c2 = { ~X2,

~X4,~X5}


Example (1/3)

Truth tableConsider the following truth table in logic (0 = false, 1 =true):

X Y Z f (X ,Y , Z)0 0 0 00 0 1 00 1 0 00 1 1 11 0 0 01 0 1 11 1 0 11 1 1 1

How can you summarize the function f (X ,Y , Z) ?


Example (2/3)

Boolean expressionYou can compute the boolean expression of f (X ,Y , Z) as :

f (X ,Y , Z) = X .Y .Z + X .Y .Z + X .Y .Z + X .Y .Z

Majority functionIt is the majority function, such that f (X ,Y , Z) = 1 if thenumber of variables (X ,Y , Z) set to 1 is greater than thenumber of variables set to 0.


Example (3/3)

Majority functionWith the majority function definition, you can extend thefunction with a greater number of input variables andkeep the same definition :

f (X ,Y , Z ,W )

f (X ,Y , Z ,W , T )

...


2. What will we cover ?


We will cover

Theoretical and practical aspects ofregressionclassificationclusteringdecision treeneural networks


We will cover

RegressionPrinciple: try tocompute a property infunction of otherpropertiesexample: weight =f (height ,age, sex)

0

5

10

15

20

0 1 2 3 4 5 6 7 8

y

x

measureslaw 2*x+3

Weka LR


We will cover

0

5

10

15

20

0 1 2 3 4 5 6 7 8

y

x

measureslaw 2*x+3

Weka LR


We will cover

ClassificationPrinciple: try to splitdata into groups usingthe properties of objets,knowing the number ofgroupspart of supervisedlearning


We will cover

ClusteringPrinciple: try to create groups of objects based ontheir similarities, the number of groups is unknownpart of unsupervised learning


We will cover

Decision treePrinciple: create amodel that predicts thevalue of a targetvariable by learningsimple decision rulesinferred from the datafeaturesa non-parametricsupervised learningmethod used forclassification andregression


We will cover


We will cover

Neural Networksalso called ArtificialNeural Networks (ANNs)or ConnectionistSystemsPrinciple: a computingdevice inspired bybiological neuralnetworks that has theability to progressivelylearn by consideringexamples (train set)kind of a black-box


Resources

SoftwareWe will use the following software:

WEKA Waikato Environment for Knowledge Analysis,http://www.cs.waikato.ac.nz/ml/weka

R (rio, dplyr, ggplot2)Python (numpy, scipy, sklearn, matplotlib)


http://www.cs.waikato.ac.nz/ml/weka

3. Skills


Required skills

Skills you need to haveBefore entering the course you should have

mathematical background (matrix, statistics,probabilities)computer science background (Java, R or Python)


Skills to acquire

Skills to acquireAfter this course you shouldbe able to

give a definition and explain what is data miningdefine the different kind of analysis / analytics of DataMiningchose the correct method / algorithm to produce ananalysis in function of the data and the question youhave to answer


3. End


University of Angers - Faculty of Sciences

UA - Angers2 Boulevard Lavoisier 49045 AngersCedex 01Tel: (+33) (0)2-41-73-50-72


Data Mining - Overviericher/dm/data_mining_1_overview.pdf · Deﬁnition 1 Data Mining (Minería de...

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