Research on Data mining at Research Group in Intelligent Systems

Data MininggResearch, Innovation and Development for supporting experts in their decisionspp g p

Supporting experts is helping them to take more effective efficient and reliable decisionseffective, efficient and reliable decisions

Page 1Research, Innovation and Development for supporting experts in their decisions

Campus laSalle Barcelonap

• Campus with 5 buildingsp g• 4000 students.• More than 100 years training highly qualified professionals.

Entrepreneurship

Differential Research ffmethodology

InternationalPrestige and

Groups

International character

La Salle

Prestige and innovation

Laboratories and infrastructures

La Salle TechnovaBarcelona


Outline

• Research Group in Intelligent SystemsDescriptors– Descriptors

– Research on Data Mining• Projects related to Health Sciences

– Decision support system for Breast Cancer – Data Mining as support for melanoma experts


What is Artificial Intelligence (AI)?

• John McCarthy coined AI term in 1956 as ‘the science and engineering ofmaking intelligent machines’ at a conference at Dartmouth College. Intelligent

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machine terms refer to the capability of performing intelligent human processesas:

– Learning– Reasoning– Problem solving– PerceptionPerception– Language understanding

• AI has become an essential part of the technology industry, providing theAI has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

– Prediction– Classification– Classification– Regression– Clustering

F ti ti i ti– Function optimization


Why is AI powerful?

• The power resides in the combination of disciplines that tackle the same

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• The power resides in the combination of disciplines that tackle the same problems as AI: learn and understand, to solve problems and to make decisions.

• AI is fed from many disciplinesPhil h L i th d f i i d h i l t– Philosophy: Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality.

– Mathematics: Formal representation and proof, algorithms, computation, ( ) ( )(un)decidability, (in)tractability.

– Statistics : Modeling uncertainty, learning from data.– Economics: Utility, decision theory, rational economic agents.y y g– Neuroscience: Neurons as information processing units.– Psychology / NeuroScience: How do people behave, perceive, process

cognitive information represent knowledgecognitive information, represent knowledge.– Computer Engineering: Building fast computers.– Control Theory: Design systems that maximize an objective function over time.– Linguistics: Knowledge representation, grammars.


Basis of Artificial Intelligenceg

Philosophy• Discussion about

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Mathematical. • Philosophic bases

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Computational linguistic

Cognitive psychology

Computational engineering

the possibility of a mechanical intelligence.

requires formal rules.

• Understanding language requires understanding of the subject matter

d th t t

• Behavior theories, rational behave basis.

• Some mechanism, hardware and tools are required for AI.

and the context.


A possible map of the current AIp p

• Evolutionary Computation• Case-Based Reasoning

R i f t L i

• Non monotonic reasoning• Model based reasoning

C t i t ti f ti • Reinforcement Learning• Neural Network

• Data Analysis

• Constraint satisfaction• Qualitative reasoning• Uncertain reasoning• Temporal reasoningTemporal reasoning• Heuristic search

Reasoning Machine Learning

Robotics,

• Logic

Robotics, perception and natural language

Knowledge Management

• Robotics and control• Natural Language Processing

• Multiagents systems• Decision Support System• Knowledge management• Knowledge representation

g gprocessing

Natural Language Processing• Artificial vision

• Speech recognition

Knowledge representation• Ontology and semantic web• Computer-Human interaction


Research Group in Intelligent Systems• GRSI is a research group focused on Machine Learning, especially in

the field of Knowledge Discovery from Databases (KDD) (also

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known as Data Mining) for extracting interesting patterns from moderate and large complex data.

– Created in 1994Created in 1994 – Recognized as consolidated by Generalitat de Catalunya since 2002.– Group is composed of 18 members.

F ll f J M í G ll i th h d f th– Full professor Josep María Garrell is the head of the group.– We tackle classification, prediction, regression, optimization,

recommendation and diagnosis problems which occur in complex and huge volume of data in domains such as….

Health Energy Telematic Learning


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Data Mining sets the differenceValue

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How can we help them?Wisdom(Knowledge+ experience)

Why they are getting worse?

( g )

Knowledge(Information+ rules)How far do you

How many patients got worse?

(Information rules)

Information(Data + Context)

want to go?

How many patients are in the Intensive Care Unit?

(Data + Context)

Data Intensive Care Unit?

KDD allow experts to extract useful and hidden knowledge from data.

Volume The approach is valid for any domainBusiness, space, communication media, insurance companies,

financial services, health sciences, games, etc.


Research lines• GRSI works on the different stages of Knowledge Data

Discovery: characterization, pre-processing, analysis for a better understanding and improvement of machine learning techniques, methodologies to evaluate learners and post-processing.

Problem Analysis

Data Analysis

D t

Knowledge

Data Processing

Production

Modeling

Evaluation


Data Mining applicationsg pp

Clustering

Knowledge discovery

Association rulesClassification

Regression


Techniquesqn C t t g S l bl s Si l t s M th

ompu

tatio

n Create computer programs inspired by the process of natural selection and genetic laws for search R

easo

ning Solves new problems

using other previously solved.E.g. Retrieve a set of al

Netw

orks Simulate some

properties of biological neural networks to replicate how ‘our neurons’ works xit

y met

rics Measure the

‘complexity’ of a problem in terms of class separability and the discriminant power

lutio

nary

Co laws for search,

optimization and machine learning.E.g. Look for the best

Case

-Bas

ed g

similar mammographic images to a expert according to a set of criteria.

Neur

a neurons works.E.g. Build system that is able to replicate a behavior based on a

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Com

plex the discriminant power

of features.E.g. Relate how the data complexity affects

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Evo equation that

represents a set of points. So

ft C set of inputs and

outputs previously known.

the performance of algorithms in order to adjust them properly.


GRSI members

DirectorGarrell Guiu Josep M PhD

Member emeritiBacardit Jaume PhD UKGarrell Guiu, Josep M., PhD

Assistant executive directorFornells Herrera, Albert, PhD

M b

Bacardit, Jaume, PhD, UKCastanys Tutzó, Mireia, PhDFarguell Matesanz, Enric, PhDLl à X i PhD USAMembers

Bernadó Mansilla, Ester, PhDCamps Dausà, Joan

Llorà, Xavier, PhD, USA Martorell Rodon, Josep Maria, PhDMacià Antolínez, Núria, PhDp ,

Corral Torruella, Guiomar, PhDGarcía Piquer, ÁlvaroGarriga Berga Carles PhD

Nettleton, David, PhDOrriols-Puig, Albert, PhD, USASalamó Llorente Maria PhDGarriga Berga, Carles, PhD

Golobardes Ribé, Elisabet, PhDNicolàs Sans, Rubén

Salamó Llorente, Maria, PhDPazienza de Filippis, Giovanni Egidio,

PhD, Hungary

Rios Boutín, JoaquimSancho Asensio, AndreuTeixidó Navarro FrancescTeixidó Navarro, FrancescVernet Bellet, David


Outline

• Research Group in Intelligent SystemsDescriptors– Descriptors

– Research on Data Mining• Projects related to health Sciences

– Decision support system for Breast Cancer – Data Mining as support for melanoma experts


Breast cancer diagnosis

• Goal: Development of a tool for intelligent retrieval of mammographic imagesby content analysis in order to help experts in the diagnosis process.

gTIC2002-04160-C02-02

by content analysis in order to help experts in the diagnosis process.

Di it li ti Mammographic capture

Digitalization, segmentation and feature

extractionRetrieval of

mammographic records Diagnosis support


Melanoma diagnosis TIN2006-15140-C03-03

• Goal: Help experts in the melanoma characterization for improving melanoma diagnosis

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Characterization Patterns Medical protocols

Decision support systemspp y


Telematic vulnerabilities TIN2006-15140-C03-03

• Goal: Provide tools to the security analyst for helping them in the security analysis tasks by means of the identification of problematic situations which are nottasks by means of the identification of problematic situations which are not obviously.

CONSENSUS ANALIA


Active Demand Management

• Goal: Integration of system (OS), distribution (OD) and sellers (CM) agents for anefficient management of demand

gCEN200710126

efficient management of demand.

OSDesign and

implementation of agent communication Design and develop

intelligent devices for the

GAD CMOD

gmanagement of energy

demand at home

CL Pattern identification Develop of specific rates for clients Analysis of client demand


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Integris: INTElligent GRId Sensor

• Goal: Integration and management of communication technologies in smart-

communications FP7 ICT-Energy-2009-1, Objective 6.5, #247938

grids for assuring QoS, security and reliability.


Thanks for your attentiony

For further information visit http://www.salle.url.edu/GRSIor send an email to afornells@salle url eduor send an email to [email protected]


Education

Research on Data mining at Research Group in Intelligent Systems