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Titlebook: Data Mining and Multi-agent Integration; Longbing Cao Book 2009 Springer-Verlag US 2009 AAMAS.Clustering.agent-enriched data mining.algori

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Christoph Buchheim,Maja Hüggingare related to the extraction, management and reuse of the huge amount ofWeb data available. These data usually has a high heterogeneity, volatility and low quality (i.e. format and content mistakes), so it is quite hard to build reliable systems. This chapter proposes an Evolutionary Computation ap
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Ibrahima Diarrassouba,Youssouf Hadhbiain aim of SKC is to select the knowledge contained in the system by paying attention to its use. This paper presents the SKC Network Module (NM), which is in charge of discovering other instances of the system on the Internet and establishing contact with them to create a knowledge network on the W
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Bounded Variation in Binary Sequences. Yet, the manual acquisition of knowledge about user goals is costly and often infeasible. In a departure from existing approaches, this paper proposes Goal Mining as a novel perspective for knowledge acquisition. The research presented in this chapter makes the following contributions: (a) it pres
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Lecture Notes in Computer Sciencebuted by many individuals and interacting under decentralized control, to address data mining requests. EMADS is seen both as an end user platform and a research tool. This chapter details the EMADS vision, the associated conceptual framework and the current implementation. Although EMADS may be app
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Spanning Trees and Arborescences,vast volumes of data is available containing enormous amount of hidden information. Generating abstractions from such large data is a challenging data mining task. Efficient large data clustering schemes are important in dealing with such large data. In the current work we provide two different effi
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Introduction to Agent Mining Interaction and Integrationen activated toward removing the boundary between them, that is the interaction and integration between agent technology and data mining. We refer this to . as a new area. The marriage of agents and data mining is driven by challenges faced by both communities, and the need of developing more advanc
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