By Longbing Cao, A.E. Gorodetsky, Jiming Liu, Gerhard Weiß, Philipp S Yu
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 could 10-15, 2009 as an linked occasion of AAMAS 2009, the eighth foreign Joint convention on self sustaining brokers and Multiagent structures. The 12 revised papers and a pair of invited talks offered have been rigorously reviewed and chosen from quite a few submissions. geared up in topical sections on agent-driven info mining, facts mining pushed brokers, and agent mining purposes, the papers convey the exploiting of agent-driven information mining and the resolving of severe info mining difficulties in concept and perform; tips on how to increase facts mining-driven brokers, and the way information mining can boost agent intelligence in study and sensible purposes. matters which are additionally addressed are exploring the mixing of brokers and information 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 knowledge mining.
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Extra 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
To normalize the data, the Z-score with standard deviation normalization method was applied. As the true bounds of the demand data in the dataset are unknown and the difference between values of various time series is high, the chosen normalization method is one of the most suitable ones. Figure 4 displays an example of time series data used in experiments. As can be seen, the time series differs not only in duration, but also in amplitude and its pattern. The main target of the performed experiments was to analyse comparatively the precision of forecasting transition points with square neural network topology with 8 neighbours applied while using different network load q.
The paper presents the Agents Based Data Mining and Decision Support system, meant for supporting a production manager in his/her production planning decisions. The developed system is based on the analysis of historical demand for products and on the information about transitions between phases in life cycles of those products. The architecture of the developed system is presented as also an analysis of testing on the real-world data results is given. Keywords: Software Agents, Data Mining, Decision Support, Forecasting Transition Points.
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.
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