Integration of Agent and Data Mining Longbing Cao University of Technology, Sydney

Content Introduction Introduction Agents can enrich data mining Agents can enrich data mining Data mining can improve agents Data mining can improve agents Ontology-based integration of agents and data mining Ontology-based integration of agents and data mining Demo Demo Conclusions and directions Conclusions and directions

INTRODUCTION

Data mining & multiagent research group at UTS Cross disciplinary researchers interacting at the group Cross disciplinary researchers interacting at the group Integrated research of data mining and multi-agent system Integrated research of data mining and multi-agent system – Real-world applications of the integration Real-world applications of the integration –Capital markets –F-Trade

Agents as a new computing paradigm for complex problems Strengths Strengths –Analyze and understand complex systems –Deal with nonfunctional requirements –Handle social complexity such as distribution, dynamics, interaction, evolution, self-organization –Build flexible infrastructure Weaknesses Weaknesses –Lack machine learning capability –Lack in-depth analytics –Lack knowledge representation

Data mining and knowledge discovery as an effective tool for in- depth analysis Strengths Strengths –Deep data analysis –Deep knowledge discovery Weaknesses Weaknesses –Nothing related to system infrastructure –Deal with social complexity such as distribution, dynamics

Bilateral enhancement of agents and data mining by the integration Agents can enrich data mining Agents can enrich data mining Data mining can improve agents Data mining can improve agents Mutual enhancement: integration between data mining and multi-agent system Mutual enhancement: integration between data mining and multi-agent system

AGENTS can ENRICH DATA MINING

Building agent-based data mining systems Agent-based data mining system Agent-based data mining system –F-Trade Agent-based distributed data mining system Agent-based distributed data mining system –Agent-based distributed data mining systems, such as BODHI, PADMA, JAM, Papyrus Agents for multiple data source mining Agents for multiple data source mining Agents for web mining Agents for web mining

Data mining models as agents Intelligent data mining agents – modeling data mining algorithms as agents Intelligent data mining agents – modeling data mining algorithms as agents Data mining model integrator – integrating data mining algorithms Data mining model integrator – integrating data mining algorithms Data mining model planner – smartly managing data mining algorithms Data mining model planner – smartly managing data mining algorithms Data mining model recommender – recommending appropriate algorithms Data mining model recommender – recommending appropriate algorithms

Agent-based mediation and management of distributed and large-scale data sources Data gateway agents for connecting data sources Data gateway agents for connecting data sources Distributed data preprocessor agent Distributed data preprocessor agent Data integrator agents for data integration Data integrator agents for data integration Agents for data clustering Agents for data clustering Agents for ensemble mining in distributed data Agents for ensemble mining in distributed data Agents for data sampling and assumption Agents for data sampling and assumption

User and interaction agents for data mining Human agent interaction for data mining Human agent interaction for data mining Agents for interactive mining Agents for interactive mining Agents in human-guided mining Agents in human-guided mining Domain knowledge management using agents Domain knowledge management using agents User agents for preparing mining reports User agents for preparing mining reports Agents for circulating mining results Agents for circulating mining results

Case 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 sources) 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-Trade System infrastructure

Case study 1 -- F-Trade F-TRADE: Financial Trading Rules Automated Development & Evaluation

Case study 1 -- F-Trade Algorithm as an agent

Case 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: PluginPerson Pre-conditions: -A request of registering an algorithm has been activated by protocol SubmitAlgoPluginRequest -A knowledge base storing rules for agent and service naming and directory Type: algorithm.[datamining/tradingsignal] Location: algo.[algorithmname] Inputs: inputlist InputConstraints: inputconstraint[;] Outputs: outputlist OutputConstraints: outputconstraint[;] Activities: Register the algorithm Permissions: -Read supplied knowledge base storing algorithm agent ontologies -Read supplied algorithm base storing algorithm information Post-conditions: -Generate unique agent identifier, naming, and locator for the algorithm agent -Generate unique algorithm id Exceptions: -Cannot find target algorithm -There are invalid format existing in the input attributes Agent plug- and-play

Case study 1 -- F-Trade Agent for multiple data sources management

Case study 1 -- F-Trade Agent for reporting

Case study 2 – agent-based WEKA

Case study 3 – ensemble

DATA MINING can IMPROVE AGENTS

Data mining-driven multiagent learning DM-driven learning in MAS DM-driven learning in MAS –Coordination learning –Individual learning –Group/collective learning –Distributed learning –Online/offline learning

Data mining-driven evolution and adaptation in MAS Evolution of MAS based on hidden rules, so mine these rules and fill into the agent knowledge base for designing evolutionary agent systems Evolution of MAS based on hidden rules, so mine these rules and fill into the agent knowledge base for designing evolutionary agent systems Adaptive capability mining for enhancing agent ’ s adaptation Adaptive capability mining for enhancing agent ’ s adaptation Self-organization rule mining Self-organization rule mining

Data mining for agent communication, planning and dispatching Cluster and classification Cluster and classification Class/segment-based communication Class/segment-based communication Class-based planning and dispatching Class-based planning and dispatching

DM-based User modeling Modeling user behavior from DM Modeling user behavior from DM –Game player modeling –Trader ’ s behavior modeling –Trader ’ s role modeling User-agent interaction based on user modeling User-agent interaction based on user modeling –Trader agents ’ interface design –Trader-agent interaction rule design

DM-based User servicing DM-based agents for serving users DM-based agents for serving users –Visualization mining for reporting –Customer-relationship management for customer care DM-based recommender agents DM-based recommender agents –Stock recommender –In-depth rule recommender –Trading rule-stock recommender

Case study - learning Agent learning via machine learning Agent learning via machine learning –Reinforcement learning –Evolutionary multiobjective methods –Evolutionary algorithm –Markov decision process –Temporal difference method

Case study – user modeling Trader ’ s behavior modeling Trader ’ s behavior modeling Trader ’ s role modeling Trader ’ s role modeling –Market order –Limit order

MarketOrderLargeMarketOrder January February Large market orders analysis

Case study - servicing Pairs trading Pairs trading –Mining correlated stock pairs –Correlated stock miner agent –Stock pairs recommender –Pairs trading strategy solution

Case study - servicing Optimized rules Optimized rules –Mining in-depth rules –In-depth rule miner agent –User interface agent –Optimized rules recommender –Optimized trading strategy solution

Case study - servicing Rule-stock pairs Rule-stock pairs –Mining rule-stock pairs –Rule-stock pair mining agent –User interface agent –Rule-stock pair recommender –Trading strategy solution

Return on investment

ONTOLOGY-BASED INTEGRATION OF AGENTS AND DATA MINING

Ontology for domain understanding and interaction Domain ontology for understanding the domain problems Domain ontology for understanding the domain problems Problem-solving ontology Problem-solving ontology Task ontology Task ontology Method ontology Method ontology

Ontology for knowledge management Ontology for organizing agent systems Ontology for organizing agent systems Ontology for organizing mining algorithms Ontology for organizing mining algorithms Ontology for user interaction Ontology for user interaction Managing domain ontology/task ontology/problem-solving ontology/method ontology Managing domain ontology/task ontology/problem-solving ontology/method ontology

Ontology-based system architecture Multi-domain ontological space Multi-domain ontological space –Related problem domains –Agent ontology domain –Data mining ontology domain Hybrid ontology structure for organizing ontologies crossing multiple domains Hybrid ontology structure for organizing ontologies crossing multiple domains

Ontological engineering for the integration Ontology namespace Ontology namespace Ontology mapping structure Ontology mapping structure Semantic rules for ontology mapping Semantic rules for ontology mapping Ontology transformation Ontology transformation Ontology query Ontology query Ontology search and discovery Ontology search and discovery

M 1 ? M 2 N 1 = N 2 || N 1  N 2 N1  N2N1  N2 M1  M2M1  M2 Equivalence, similarity Synonyms, encoding, conventions, paradigms, scaling M1  M2M1  M2 Scope, coverage, granularity Generalization, specialization  =M 1  M 2 Naming conflict, homonymy Disjointness, antonyms  M 1  M 2 <min(M 1, M 2 ) Scope, coverage, granularity Overlapping M1  M2M1  M2 Naming, encodingInstantiation -  (part_of (A, B)  part_of (B, C))  part_of (A, C) -  (substitute_to (A, B)  substitute_to (B, C))  substitute_to (A, C)

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

DEMO

CONCLUSIONS and DIRECTIONS