Integration of Agent and Integration of Agent and Data MiningData Mining

Longbing CaoLongbing Cao

University of Technology, SydneyUniversity of Technology, Sydney

ContentContent

• IntroductionIntroduction

• Agents can enrich data miningAgents can enrich data mining

• Data mining can improve agentsData mining can improve agents

• Ontology-based integration of agents Ontology-based integration of agents and data mining and data mining

• DemoDemo

• Conclusions and directionsConclusions and directions

INTRODUCTIONINTRODUCTION

Data mining & multiagent researcData mining & multiagent research group at UTSh group at UTS

• Cross disciplinary researchers interacting at the Cross disciplinary researchers interacting at the groupgroup

• Integrated research of data mining and multi-Integrated research of data mining and multi-agent systemagent system– http://datamining.it.uts.edu.auhttp://datamining.it.uts.edu.au

• Real-world applications of the integrationReal-world applications of the integration– Capital marketsCapital markets– F-TradeF-Trade

Agents as a new computing Agents as a new computing paradigm for complex paradigm for complex

problemsproblems• StrengthsStrengths

– Analyze and understand complex systemsAnalyze and understand complex systems– Deal with nonfunctional requirementsDeal with nonfunctional requirements– Handle social complexity such as distribution, Handle social complexity such as distribution,

dynamics, interaction, evolution, self-dynamics, interaction, evolution, self-organizationorganization

– Build flexible infrastructureBuild flexible infrastructure

• WeaknessesWeaknesses– Lack machine learning capabilityLack machine learning capability– Lack in-depth analyticsLack in-depth analytics– Lack knowledge representationLack knowledge representation

Data mining and knowledge Data mining and knowledge discovery as an effective tool discovery as an effective tool

for in-depth analysisfor in-depth analysis• StrengthsStrengths

– Deep data analysisDeep data analysis– Deep knowledge discoveryDeep knowledge discovery

• WeaknessesWeaknesses– Nothing related to system infrastructureNothing related to system infrastructure– Deal with social complexity such as Deal with social complexity such as

distribution, dynamicsdistribution, dynamics

Bilateral enhancement of Bilateral enhancement of agents and data mining by the agents and data mining by the

integrationintegration

• Agents can enrich data miningAgents can enrich data mining

• Data mining can improve agentsData mining can improve agents

• Mutual enhancement: integration Mutual enhancement: integration between data mining and multi-between data mining and multi-agent systemagent system

AGENTS can ENRICH DATA MININGAGENTS can ENRICH DATA MINING

Building agent-based data Building agent-based data mining systemsmining systems

• Agent-based data mining systemAgent-based data mining system– F-TradeF-Trade

• Agent-based distributed data mining Agent-based distributed data mining systemsystem– Agent-based distributed data mining Agent-based distributed data mining

systems, such as BODHI, PADMA, JAM, systems, such as BODHI, PADMA, JAM, PapyrusPapyrus

• Agents for multiple data source mining Agents for multiple data source mining • Agents for web mining Agents for web mining

Data mining models as Data mining models as agentsagents

• Intelligent data mining agents – Intelligent data mining agents – modeling data mining algorithms as modeling data mining algorithms as agentsagents

• Data mining model integrator – Data mining model integrator – integrating data mining algorithms integrating data mining algorithms

• Data mining model planner – smartly Data mining model planner – smartly managing data mining algorithmsmanaging data mining algorithms

• Data mining model recommender – Data mining model recommender – recommending appropriate algorithmsrecommending appropriate algorithms

Agent-based mediation and Agent-based mediation and management of distributed management of distributed

and large-scale data sourcesand large-scale data sources• Data gateway agents for connecting data Data gateway agents for connecting data

sourcessources• Distributed data preprocessor agentDistributed data preprocessor agent• Data integrator agents for data Data integrator agents for data

integrationintegration• Agents for data clusteringAgents for data clustering• Agents for ensemble mining in Agents for ensemble mining in

distributed datadistributed data• Agents for data sampling and assumptionAgents for data sampling and assumption

User and interaction agents for User and interaction agents for data miningdata mining

• Human agent interaction for data miningHuman agent interaction for data mining

• Agents for interactive miningAgents for interactive mining

• Agents in human-guided miningAgents in human-guided mining

• Domain knowledge management using Domain knowledge management using agentsagents

• User agents for preparing mining reportsUser agents for preparing mining reports

• Agents for circulating mining resultsAgents for circulating mining results

Case study 1 -- F-TradeCase study 1 -- F-Trade

Users/CMCRC/Instituations(Anybody,anytime,anywhere, from MAS & KDD & Finance)

Applications developers

Network(Internet &

LAN)

Data Sources(Diff. Providers: AC3, HK

market, CSFB, etc. Diff. Formats: FAV, ODBC,

JDBC, OLEDB, etc.)

F-Trade(open automated

enterprise services, and personalized services)

AAMAS Researchers(OCAS, AOSE, OADI, OSOAD)(Services for system components,algorithm and multiple data sou

rces)

KDD Researchers(Frequent and abnormal patterns

discovery, optimization of trading strategies, correlation

analysis)

Aims/Motivations:

• Research Service Provider for AAMAS and data mining

• Integrated Infrastructure for Financial Trading and Mining Support

Case study 1 -- F-TradeCase study 1 -- F-Trade

System infrastructure

Case study 1 -- F-TradeCase study 1 -- F-Trade

F-TRADE: Financial Trading Rules Automated Development & Evaluation

Case study 1 -- F-TradeCase study 1 -- F-Trade

Algorithm as an agent

Case study 1 -- F-TradeCase study 1 -- F-Trade

AgentService RegisterAlgorithm(algoname;inputlist;inputconstraint;outputlist;outputconstraint;)Description: This agent service involves accepting registration application submitted by role PluginPerson, checking validity of attribute items, creating name and directory of the algorithm, and generating universal agent identifier and unique algorithm id. Role: PluginPersonPre-conditions:

-A request of registering an algorithm has been activated by protocol SubmitAlgoPluginRequest

-A knowledge base storing rules for agent and service naming and directoryType: algorithm.[datamining/tradingsignal]Location: algo.[algorithmname]Inputs: inputlistInputConstraints: inputconstraint[;]Outputs: outputlistOutputConstraints: outputconstraint[;]Activities: Register the algorithmPermissions:

-Read supplied knowledge base storing algorithm agent ontologies-Read supplied algorithm base storing algorithm informationPost-conditions:

-Generate unique agent identifier, naming, and locator for the algorithm agent-Generate unique algorithm idExceptions:

-Cannot find target algorithm-There are invalid format existing in the input attributes

Agent plug-and-play

Case study 1 -- F-TradeCase study 1 -- F-Trade

Agent for multiple data sources management

Case study 1 -- F-TradeCase study 1 -- F-Trade

Visual Reports Point Reports Transactions Reports Summary Reports Input Reports

Agent for reporting

Case study 2 – agent-based Case study 2 – agent-based WEKAWEKA

Case study 3 – ensemble Case study 3 – ensemble

DATA MINING can IMPROVE DATA MINING can IMPROVE AGENTSAGENTS

Data mining-driven multiagent leData mining-driven multiagent learningarning

• DM-driven learning in MASDM-driven learning in MAS– Coordination learningCoordination learning– Individual learningIndividual learning– Group/collective learningGroup/collective learning– Distributed learningDistributed learning– Online/offline learningOnline/offline learning

Data mining-driven evolution Data mining-driven evolution and adaptation in MASand adaptation in MAS

• Evolution of MAS based on hidden Evolution of MAS based on hidden rules, so mine these rules and fill into rules, so mine these rules and fill into the agent knowledge base for the agent knowledge base for designing evolutionary agent designing evolutionary agent systemssystems

• Adaptive capability mining for Adaptive capability mining for enhancing agent’s adaptationenhancing agent’s adaptation

• Self-organization rule miningSelf-organization rule mining

Data mining for agent Data mining for agent communication, planning and communication, planning and

dispatchingdispatching

• Cluster and classificationCluster and classification

• Class/segment-based communicationClass/segment-based communication

• Class-based planning and Class-based planning and dispatchingdispatching

DM-based User modelingDM-based User modeling

• Modeling user behavior from DMModeling user behavior from DM– Game player modelingGame player modeling– Trader’s behavior modelingTrader’s behavior modeling– Trader’s role modelingTrader’s role modeling

• User-agent interaction based on user User-agent interaction based on user modelingmodeling– Trader agents’ interface designTrader agents’ interface design– Trader-agent interaction rule design Trader-agent interaction rule design

DM-based User servicingDM-based User servicing

• DM-based agents for serving usersDM-based agents for serving users– Visualization mining for reportingVisualization mining for reporting– Customer-relationship management for Customer-relationship management for

customer care customer care

• DM-based recommender agentsDM-based recommender agents– Stock recommenderStock recommender– In-depth rule recommenderIn-depth rule recommender– Trading rule-stock recommenderTrading rule-stock recommender

Case study - learningCase study - learning

• Agent learning via machine learningAgent learning via machine learning– Reinforcement learningReinforcement learning– Evolutionary multiobjective methodsEvolutionary multiobjective methods– Evolutionary algorithmEvolutionary algorithm– Markov decision processMarkov decision process– Temporal difference methodTemporal difference method

Case study – user modelingCase study – user modeling

• Trader’s behavior modelingTrader’s behavior modeling

• Trader’s role modelingTrader’s role modeling– Market orderMarket order– Limit orderLimit order

MarketOrderMarketOrder LargeMarketOrdLargeMarketOrderer

JanuaryJanuary

February

Large market orders analysisLarge market orders analysis

Case study - servicingCase study - servicing

• Pairs tradingPairs trading– Mining correlated stock pairsMining correlated stock pairs– Correlated stock miner agentCorrelated stock miner agent

– Stock pairs recommenderStock pairs recommender– Pairs trading strategy solutionPairs trading strategy solution

Case study - servicingCase study - servicing

• Optimized rulesOptimized rules– Mining in-depth rulesMining in-depth rules– In-depth rule miner agentIn-depth rule miner agent– User interface agentUser interface agent

– Optimized rules recommenderOptimized rules recommender– Optimized trading strategy solutionOptimized trading strategy solution

Case study - servicingCase study - servicing

• Rule-stock pairsRule-stock pairs– Mining rule-stock pairsMining rule-stock pairs– Rule-stock pair mining agentRule-stock pair mining agent– User interface agentUser interface agent

– Rule-stock pair recommenderRule-stock pair recommender– Trading strategy solutionTrading strategy solution

Return on investment

ONTOLOGY-BASED INTEGRATION ONTOLOGY-BASED INTEGRATION OF AGENTS AND DATA MININGOF AGENTS AND DATA MINING

Ontology for domain Ontology for domain understanding and understanding and

interactioninteraction• Domain ontology for understanding Domain ontology for understanding

the domain problemsthe domain problems

• Problem-solving ontology Problem-solving ontology

• Task ontologyTask ontology

• Method ontologyMethod ontology

Ontology for knowledge Ontology for knowledge managementmanagement

• Ontology for organizing agent Ontology for organizing agent systemssystems

• Ontology for organizing mining Ontology for organizing mining algorithmsalgorithms

• Ontology for user interactionOntology for user interaction

• Managing domain ontology/task Managing domain ontology/task ontology/problem-solving ontology/problem-solving ontology/method ontologyontology/method ontology

Ontology-based system Ontology-based system architecturearchitecture

•Multi-domain ontological spaceMulti-domain ontological space– Related problem domainsRelated problem domains– Agent ontology domainAgent ontology domain– Data mining ontology domainData mining ontology domain

•Hybrid ontology structure for organiHybrid ontology structure for organizing ontologies crossing multiple dozing ontologies crossing multiple domainsmains

Ontological engineering for Ontological engineering for the integrationthe integration

•Ontology namespaceOntology namespace

•Ontology mapping structureOntology mapping structure

•Semantic rules for ontology Semantic rules for ontology mappingmapping

•Ontology transformationOntology transformation

•Ontology queryOntology query

•Ontology search and discoveryOntology search and discovery

Business Profiles

Domain Ontology

Businesslogic View

BL Ontology

Task View

Task Ontology

Resource View

Resource Ontology

Internal PSO linkage DO-to-PSO linkage

Problemsolving System

PS Ontology

Method View

Method Ontology

Stock

Stop Order

FinancialOrder

Market OrderLimit Order

...

OrderOperation

Amend Enter Trade Delete Cantr

Price Dealer Date Time Volume

subc

lass-o

f

part-

of

instan

ce-o

f

Algorithm Agent

Outputontologies

Inputontologies

...

...

...

......

….

Resourceontologies

Knowledgeontologies

M1 ? M2 N1 = N2 || N1 N2 N1 N2

M1 M2 Equivalence, similarity

Synonyms, encoding, conventions, paradigms,

scaling

M1 M2 Scope, coverage, granularity

Generalization, specialization

=M1M2 Naming conflict, homonymy

Disjointness, antonyms

M1M2

<min(M1,

M2)

Scope, coverage, granularity

Overlapping

M1 M2 Naming, encoding Instantiation

- (part_of (A, B) part_of (B, C)) part_of (A, C)

- (substitute_to (A, B) substitute_to (B, C)) substitute_to (A, C)

Root Concept - fixed - resident - id - local_fee - remote_fee - IP - business

Root Concept - home - local_call - intraprovince - interprovince - international - Hongkong - Taiwan - Macau - IP - business

Ontology 1 Ontology 2

concept-to-concept

attribute-to-concept

attribute-to-attribute

1 attribute-to-m*attribute

Rule 4. - (A AND B), B ::= substitute_to(A, B) A OR B, the resulting output is A or BRule 5. - (A AND B), B ::= disjoint_to(A, B) A AND B, the resulting output is A and

B

DEMODEMO

CONCLUSIONS and DIRECTIONSCONCLUSIONS and DIRECTIONS