By Longbing Cao, A.E. Gorodetsky, Jiming Liu, Gerhard Weiß, Philipp S Yu
This ebook 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 may perhaps 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth foreign Joint convention on self reliant brokers and Multiagent platforms. The 12 revised papers and a pair of invited talks provided have been conscientiously reviewed and chosen from a variety of submissions. equipped in topical sections on agent-driven information mining, facts mining pushed brokers, and agent mining functions, the papers express the exploiting of agent-driven information mining and the resolving of serious information mining difficulties in thought and perform; the best way to increase facts mining-driven brokers, and the way facts mining can improve agent intelligence in learn and functional functions. topics which are additionally addressed are exploring the combination of brokers and knowledge mining in the direction of a super-intelligent info processing and platforms, and picking demanding situations and instructions for destiny examine at the synergy among brokers and knowledge mining.
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Extra info for Agents and Data Mining Interaction: 4th International Workshop on Agents and Data Mining Interaction, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised
The actions, performed by the Data Mining Agent, cover such processes as initialization of a training process, performing system training and testing processes, imitation of On-line data flowing during system training and testing, Knowledge base creation and maintaining it up-to-date. The knowledge base of the Data Mining Agent is based on the modular self-organising maps, placed in the Neural Block. Each neural net either can handle records, with equal duration l or can proceed with time series with different durations.
The network organization contains two main processes - initial organization followed by a convergence process. The initial organisation takes about 1000 iterations . 01. The number of iterations the convergence process takes is at least 500 times larger than the number of neurons in the network . The main difference from the initial organization is that during the convergence process the topological neighbourhood of a winner neuron contains only the closest neighbours or just the winner neuron.
Each data record is marked with one or both markers - transition indicators ; namely, M1 indicates the period when product switched from Introduction phase to Maturity phase; and M2 indicates the period when product switched from Maturity phase to End-of-Life phase. Marks on transitions can guarantee that a model will be build; if we have patterns of transitions in historical data, then, theoretically, in presence of a model for generalisation, we are able to recognise those patterns in new data.
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