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

This publication constitutes the completely refereed post-conference complaints of the 4th foreign Workshop on brokers and information Mining interplay, ADMI 2009, held in Budapest, Hungary in might 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth overseas Joint convention on independent brokers and Multiagent Systems.

The 12 revised papers and a couple of invited talks offered have been rigorously reviewed and chosen from a variety of submissions. geared up in topical sections on agent-driven facts mining, facts mining pushed brokers, and agent mining purposes, the papers exhibit the exploiting of agent-driven information mining and the resolving of severe information mining difficulties in conception and perform; the right way to increase info mining-driven brokers, and the way facts mining can boost agent intelligence in study and useful purposes. topics which are additionally addressed are exploring the mixing of brokers and information mining in the direction of a super-intelligent info processing and structures, and making a choice on demanding situations and instructions for destiny learn at the synergy among brokers and information mining.

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

Example text

A task of product life cycle phase transition point forecasting can serve as an example of such problem where both Data Mining and Decision Support technologies should be applied. From the viewpoint of the management it is important to know, in which particular phase the product is. One of applications of that knowledge is selection of the production planning policy for the particular phase [12]. For example, for the maturity phase in case of determined demand changing boundaries it is possible to apply cyclic L.

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. 3 Aspects of Domain Intelligence In the mainstream of agents and data mining, the consideration of domain intelligence is mainly embodied through involving domain knowledge, prior knowledge, or mining the process and/or workflow associated with a business problem.

W j (n + 1) = w j (n) + η (n) · h j,i(d)(n) · (d − w j (n)) , (7) where η - learning rate parameter; d - discrete time series from learning dataset. Note how the difference between discrete time series and the vector of synaptic weights is calculated in expression (7). When the load is q = 1, that is when each neural network is processing discrete time series with a certain fixed duration, and DTW is not used, the difference between d and w j (n) is calculated as the difference between vectors of equal length.

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