Agents and Data Mining Interaction: 4th International by Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky,

By Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky, Jiming Liu, Gerhard Weiss, Philip S. Yu (eds.)

This e-book constitutes the completely refereed post-conference lawsuits of the 4th foreign Workshop on brokers and knowledge Mining interplay, ADMI 2009, held in Budapest, Hungary in might 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth foreign Joint convention on self sufficient brokers and Multiagent Systems.

The 12 revised papers and a pair of invited talks offered have been conscientiously reviewed and chosen from a variety of submissions. geared up in topical sections on agent-driven information mining, facts mining pushed brokers, and agent mining purposes, the papers express the exploiting of agent-driven info mining and the resolving of serious facts mining difficulties in idea and perform; the best way to increase info mining-driven brokers, and the way information mining can develop agent intelligence in study and useful purposes. topics which are additionally addressed are exploring the combination of brokers and knowledge mining in the direction of a super-intelligent details processing and platforms, and picking out demanding situations and instructions for destiny examine at the synergy among brokers and information mining.

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Extra resources for Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers

Sample text

As the dataset is received it being sent to the Data Mining Agent with command ”Find Best Matching Cluster”. The Data Mining Agent searches the knowledge base for the Best Matching Cluster for each of the demand time series and returns a list of found clusters to the Decision Analysis Agent. The information from the BMC contains a list of possible transition points for each analysed product - for the products in the introduction phase the M1 transition point is supplied and M2 transition point - for products in the maturity phase.

The roles of involving domain intelligence in agents, data mining and agent mining are multi-form. – Assisting in the modeling and evaluation of the problem. An example is “my trading pattern can beat the market index return” when domain intelligence of “beat market index return” is applied to evaluate a trading pattern. – Making mining realistic and business-friendly. By considering domain knowledge, we are able to work on an actual business problem rather than an artificial one abstracted from an actual problem.

Let us describe the processes hidden behind each of the main steps. 44 S. Parshutin and A. Borisov Fig. 3. Decision Analysis Agent functioning diagram Step 1: Determination of the BMC for each of the evolving products. The Decision Analysis Agent sends a request to the Data Management Agent and receives a dataset containing evolving products. Each record is preprocessed and formatted by the Data Management Agent. As the dataset is received it being sent to the Data Mining Agent with command ”Find Best Matching Cluster”.

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