Integration of Agent and Data Mining

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

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

3. INTRODUCTION

4. Data mining & multiagent research group at UTS • Cross disciplinary researchers interacting at the group • Integrated research of data mining and multi-agent system • http://datamining.it.uts.edu.au • Real-world applications of the integration • Capital markets • F-Trade

6. Agents as a new computing paradigm for complex problems • 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 • Lack machine learning capability • Lack in-depth analytics • Lack knowledge representation

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

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

9. AGENTS can ENRICH DATA MINING

10. Building agent-based data mining systems • Agent-based data mining system • F-Trade • 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 web mining

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

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

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

14. Users/CMCRC/Instituations (Anybody,anytime,anywhere, from MAS & KDD & Finance) Applications developers Network (Internet & LAN) F-Trade (open automated enterprise services, and personalized services) KDD Researchers (Frequent and abnormal patterns discovery, optimization of trading strategies, correlation analysis) AAMAS Researchers (OCAS, AOSE, OADI, OSOAD) (Services for system components,algorithm and multiple data sources) Data Sources (Diff. Providers: AC3, HK market, CSFB, etc. Diff. Formats: FAV, ODBC, JDBC, OLEDB, etc. ) Case study 1 -- F-Trade • Aims/Motivations: • Research Service Provider for AAMAS and data mining • Integrated Infrastructure for Financial Trading and Mining Support

15. Case study 1 -- F-Trade System infrastructure

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

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

18. 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

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

20. Case study 1 -- F-Trade Agent for reporting

21. Case study 2 – agent-based WEKA

22. Case study 3 – ensemble

23. DATA MINING can IMPROVE AGENTS

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

25. 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 • Adaptive capability mining for enhancing agent’s adaptation • Self-organization rule mining

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

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

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

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

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

31. MarketOrder LargeMarketOrder January February Large market orders analysis

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

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

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

37. Return on investment

38. ONTOLOGY-BASED INTEGRATION OF AGENTS AND DATA MINING

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

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

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

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

45. -  (part_of (A, B) part_of (B, C)) part_of (A, C) •  (substitute_to (A, B) substitute_to (B, C))  • substitute_to (A, C)

46. 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

48. DEMO

49. CONCLUSIONS and DIRECTIONS