Download Agents and Data Mining Interaction: 4th International by Longbing Cao, A.E. Gorodetsky, Jiming Liu, Gerhard Weiß, PDF

By Longbing Cao, A.E. Gorodetsky, Jiming Liu, Gerhard Weiß, Philipp S Yu

ISBN-10: 3642036023

ISBN-13: 9783642036026

This booklet constitutes the completely refereed post-conference court cases of the 4th overseas Workshop on brokers and information Mining interplay, ADMI 2009, held in Budapest, Hungary in could 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth overseas Joint convention on self sustaining brokers and Multiagent structures. The 12 revised papers and a couple of invited talks provided have been conscientiously reviewed and chosen from quite a few submissions. prepared in topical sections on agent-driven information mining, facts mining pushed brokers, and agent mining purposes, the papers convey the exploiting of agent-driven info mining and the resolving of serious info mining difficulties in thought and perform; the best way to increase information mining-driven brokers, and the way info mining can enhance agent intelligence in study and functional functions. matters which are additionally addressed are exploring the mixing of brokers and information mining in the direction of a super-intelligent details processing and structures, and determining demanding situations and instructions for destiny learn at the synergy among brokers and knowledge mining.

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

Example text

To calculate the logical error, it is necessary to define logically correct and logically incorrect decisions. As applied to the task of forecasting product life cycle phase transition period, logically correct and logically incorrect decisions could be described as follows: 1. Assume that discrete time series d has a duration equal to ld , but the value of the key parameter - the period of product life cycle phase transition, is p = pd , where pd > ld . This statement means that a real time of transition between the phases of the product life cycle has not come yet.

The Data Management Agent performs several tasks of managing data. It has a link to the database that contains the product demand data and regularly is updated. The Data Management Agent handles the preprocessing of the data - data normalization, exclusion of obvious outliers and data transformation to defined format before sending it to the Data Mining Agent. Such preprocessing allows 38 S. Parshutin and A. Borisov Fig. 1. Structure of the system to lessen the impact of noisiness and dominance of data [14,16].

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”. 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.

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Agents and Data Mining Interaction: 4th International Workshop on Agents and Data Mining Interaction, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised by Longbing Cao, A.E. Gorodetsky, Jiming Liu, Gerhard Weiß, Philipp S Yu


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