Download e-book for kindle: 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.)

ISBN-10: 3642036031

ISBN-13: 9783642036033

This publication constitutes the completely refereed post-conference complaints of the 4th overseas 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 reliant brokers and Multiagent Systems.

The 12 revised papers and a pair of invited talks offered have been rigorously reviewed and chosen from various submissions. geared up in topical sections on agent-driven facts mining, facts mining pushed brokers, and agent mining functions, the papers exhibit the exploiting of agent-driven info mining and the resolving of severe info mining difficulties in conception and perform; find out how to increase information mining-driven brokers, and the way info mining can increase agent intelligence in study and functional functions. matters which are additionally addressed are exploring the combination of brokers and information mining in the direction of a super-intelligent details processing and structures, and opting for demanding situations and instructions for destiny learn 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

Example text

For simplification, the index of d is omitted. Time series d represents a particular phase of a product life cycle, say introduction. Assume that for a particular transition, like introduction to maturity, a set of possible transition points P = {p1 , . . , pk , . . , pm } is available. Having such assumptions the forecasting of a transition point for a new product, represented by a time series d ∈ / D, will start with finding an implication between historical datasets D and P, f : D → P; followed by application of found model to new data.

The number of synaptic weights of a neuron in the classical Kohonen map equals to the length of records - the duration of a time series, in the input dataset [7,9,13]. Due to such limitations, it is possible to use the classical Kohonen map in the developed system only when q = 1 for each neural network . For to be able to maintain the system functionality while q > 1, it is necessary to apply some modifications to the classical Kohonen map. A heuristic, based on substituting the distance measure, is considered in this work.

Pk , . . , pm } is available. Having such assumptions the forecasting of a transition point for a new product, represented by a time series d ∈ / D, will start with finding an implication between historical datasets D and P, f : D → P; followed by application of found model to new data. 3 Structure of the System The developed system contains three main elements - Data Management Agent , Data Mining Agent and Decision Analysis Agent , shown in Figure 1. Data Management Agent. The Data Management Agent performs several tasks of managing data.

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Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers by Ana L. C. Bazzan (auth.), Longbing Cao, Vladimir Gorodetsky, Jiming Liu, Gerhard Weiss, Philip S. Yu (eds.)


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