標(biāo)題: Titlebook: Data Mining and Multi-agent Integration; Longbing Cao Book 2009 Springer-Verlag US 2009 AAMAS.Clustering.agent-enriched data mining.algori [打印本頁] 作者: 債務(wù)人 時(shí)間: 2025-3-21 18:29
書目名稱Data Mining and Multi-agent Integration影響因子(影響力)
書目名稱Data Mining and Multi-agent Integration影響因子(影響力)學(xué)科排名
書目名稱Data Mining and Multi-agent Integration網(wǎng)絡(luò)公開度
書目名稱Data Mining and Multi-agent Integration網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Mining and Multi-agent Integration被引頻次
書目名稱Data Mining and Multi-agent Integration被引頻次學(xué)科排名
書目名稱Data Mining and Multi-agent Integration年度引用
書目名稱Data Mining and Multi-agent Integration年度引用學(xué)科排名
書目名稱Data Mining and Multi-agent Integration讀者反饋
書目名稱Data Mining and Multi-agent Integration讀者反饋學(xué)科排名
作者: 情感 時(shí)間: 2025-3-21 20:30 作者: 窒息 時(shí)間: 2025-3-22 00:38 作者: 人造 時(shí)間: 2025-3-22 07:58
Book 2009 to man age such data sources for data access, monitoring, integration, and pattern merging from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an e作者: RLS898 時(shí)間: 2025-3-22 09:47 作者: 針葉 時(shí)間: 2025-3-22 14:24 作者: 針葉 時(shí)間: 2025-3-22 19:18 作者: 徹底檢查 時(shí)間: 2025-3-22 23:16
g from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an e978-1-4899-8440-1978-1-4419-0522-2作者: Tincture 時(shí)間: 2025-3-23 02:19 作者: invert 時(shí)間: 2025-3-23 09:19
http://image.papertrans.cn/d/image/262941.jpg作者: 寬大 時(shí)間: 2025-3-23 10:12
https://doi.org/10.1007/978-1-4419-0522-2AAMAS; Clustering; agent-enriched data mining; algorithm; algorithms; automated reasoning; classification; 作者: Chandelier 時(shí)間: 2025-3-23 16:47 作者: resilience 時(shí)間: 2025-3-23 19:57
Spanning Trees and Arborescences,en 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作者: institute 時(shí)間: 2025-3-23 23:36 作者: Introvert 時(shí)間: 2025-3-24 02:51
Kristóf Bérczi,Tamás Király,Simon Omlornment must encounter great dynamics due to changes in the system can affect the overall performance of the system. Agent computing whose aim is to deal with complex systems has revealed opportunities to improve distributed data mining systems in a number of ways. This paper surveys the integration o作者: CURT 時(shí)間: 2025-3-24 10:35 作者: 護(hù)航艦 時(shí)間: 2025-3-24 10:40 作者: 悲痛 時(shí)間: 2025-3-24 18:55
Spanning Trees and Arborescences,ed KnowCat. The proposed MAS employs Web Use Mining and Web Structure Mining techniques in order to detect the most relevant interactions of the Know- Cat users and therefore should have more weight in the Knowledge Crystallization mechanism of KnowCat. More concretely, the MAS extracts the users in作者: 我悲傷 時(shí)間: 2025-3-24 19:33 作者: epidermis 時(shí)間: 2025-3-25 01:02 作者: 思鄉(xiāng)病 時(shí)間: 2025-3-25 07:17
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作者: Graves’-disease 時(shí)間: 2025-3-25 08:42
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作者: Aggressive 時(shí)間: 2025-3-25 11:42
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作者: 斜坡 時(shí)間: 2025-3-25 17:29 作者: 并入 時(shí)間: 2025-3-25 21:09
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作者: amputation 時(shí)間: 2025-3-26 02:06 作者: 螢火蟲 時(shí)間: 2025-3-26 06:18
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作者: inquisitive 時(shí)間: 2025-3-26 10:13 作者: 廣口瓶 時(shí)間: 2025-3-26 15:08
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作者: 埋葬 時(shí)間: 2025-3-26 20:33 作者: 衣服 時(shí)間: 2025-3-26 23:11
Agent-Based Distributed Data Mining: A Surveynment must encounter great dynamics due to changes in the system can affect the overall performance of the system. Agent computing whose aim is to deal with complex systems has revealed opportunities to improve distributed data mining systems in a number of ways. This paper surveys the integration o作者: 使閉塞 時(shí)間: 2025-3-27 02:04 作者: 低三下四之人 時(shí)間: 2025-3-27 07:19 作者: 萬靈丹 時(shí)間: 2025-3-27 11:15
A Multi-Agent System for Extracting and Analysing Users’ Interaction in a Collaborative Knowledge Maed KnowCat. The proposed MAS employs Web Use Mining and Web Structure Mining techniques in order to detect the most relevant interactions of the Know- Cat users and therefore should have more weight in the Knowledge Crystallization mechanism of KnowCat. More concretely, the MAS extracts the users in作者: 膽小懦夫 時(shí)間: 2025-3-27 13:39
Towards Information Enrichment through Recommendation Sharingganisation based on the organisation’s datasets only. Very often the datasets of a single organisation do not have sufficient resources to be used to generate quality recommendations. Therefore, it would be beneficial if recommender systems of different organisations with similar nature can cooperat作者: 下垂 時(shí)間: 2025-3-27 21:32
A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classificat network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data 作者: adequate-intake 時(shí)間: 2025-3-27 22:00
Automatic Web Data Extraction Based on Genetic Algorithms and Regular Expressionsare 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作者: 錯(cuò)事 時(shí)間: 2025-3-28 04:57
Establishment and Maintenance of a Knowledge Network by Means of Agents and Implicit Dataain 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作者: COMA 時(shí)間: 2025-3-28 07:48 作者: 柔軟 時(shí)間: 2025-3-28 12:21
A Multi-Agent Learning Paradigm for Medical Data Mining Diagnostic Workbenchge technologies - data mining and multi-agent. To fulfill this effort, . .DiaMAS – an intelligent and interactive diagnostic workbench with multi-agent strategy, has been designed and partially implemented, where multi-agent approach can perfectly compensate most data mining methods that are only ca作者: Ancestor 時(shí)間: 2025-3-28 16:52
The EMADS Extendible Multi-Agent Data Mining Frameworkbuted 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作者: 總 時(shí)間: 2025-3-28 21:08
A Multiagent Approach to Adaptive Continuous Analysis of Streaming Data in Complex Uncertain Environ-hard optimization problem for general metric spaces and is computationally intractable for real-world problems of practical interest. The primary contribution of this work is a multi-agent method for continuous agglomerative hierarchical clustering of streaming data, and a knowledge-based selforgan作者: 沒有準(zhǔn)備 時(shí)間: 2025-3-29 02:28
Multiagent Systems for Large Data Clusteringvast 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作者: MOAN 時(shí)間: 2025-3-29 06:05
A Multiagent, Multiobjective Clustering Algorithmios involving many agents. This approach is based on independent ant colonies, each one trying to optimize one particular feature objective. The multiobjective clustering process is performed by combining the results of all colonies. Experimental evaluation shows that MACC is able to find better res作者: 一個(gè)姐姐 時(shí)間: 2025-3-29 11:05
Agent-Based Distributed Data Mining: A Surveyl with complex systems has revealed opportunities to improve distributed data mining systems in a number of ways. This paper surveys the integration of multi-agent system and distributed data mining, also known as agent-based distributed data mining, in terms of significance, system overview, existing systems, and research trends.作者: TEN 時(shí)間: 2025-3-29 14:12 作者: Immortal 時(shí)間: 2025-3-29 16:49 作者: LARK 時(shí)間: 2025-3-29 23:38 作者: backdrop 時(shí)間: 2025-3-30 01:52 作者: TERRA 時(shí)間: 2025-3-30 04:07
Ibrahima Diarrassouba,Youssouf Hadhbieb. In order to do this, each instance of the system is represented by a software agent, which is in charge of interacting with Web search engines and collaborating with the agents that represent other system instances, thereby using data mining techniques. As a result, SKC manages to build and maintain a network node to share knowledge.作者: Aprope 時(shí)間: 2025-3-30 10:28 作者: 流動才波動 時(shí)間: 2025-3-30 13:47
Spanning Trees and Arborescences,cient approaches of multiagent based large pattern clustering that would generate abstraction with single database scan, integrating domain knowledge, multiagent systems, data mining and intelligence through agent-mining interaction. We illustrate the approaches based on implementation on practical data. 作者: deactivate 時(shí)間: 2025-3-30 18:45 作者: 肥料 時(shí)間: 2025-3-30 23:34
Kristóf Bérczi,Tamás Király,Simon Omlorl with complex systems has revealed opportunities to improve distributed data mining systems in a number of ways. This paper surveys the integration of multi-agent system and distributed data mining, also known as agent-based distributed data mining, in terms of significance, system overview, existing systems, and research trends.作者: 閃光你我 時(shí)間: 2025-3-31 01:27 作者: 細(xì)頸瓶 時(shí)間: 2025-3-31 08:11
Michael Z. Zgurovsky,Alexander A. Pavlovobjective clustering process is performed by combining the results of all colonies. Experimental evaluation shows that MACC is able to find better results than the case where colonies optimize a single objective separately. 作者: 擦試不掉 時(shí)間: 2025-3-31 10:33
Book 2009t, agent min ing). The book was motivated by increasing interest and work in the agents data min ing, and vice versa. The interaction and integration comes about from the intrinsic challenges faced by agent technology and data mining respectively; for instance, multi agent systems face the problem o作者: 燈絲 時(shí)間: 2025-3-31 13:46 作者: 一回合 時(shí)間: 2025-3-31 18:48
Automatic Web Data Extraction Based on Genetic Algorithms and Regular Expressionsproach to the problem of automatically learn software entities based on Genetic Algorithms and regular expressions. These entities, also called ., will be able to extract some kind of Web data structures from examples. 作者: Ligament 時(shí)間: 2025-4-1 01:26
Establishment and Maintenance of a Knowledge Network by Means of Agents and Implicit Dataeb. In order to do this, each instance of the system is represented by a software agent, which is in charge of interacting with Web search engines and collaborating with the agents that represent other system instances, thereby using data mining techniques. As a result, SKC manages to build and maintain a network node to share knowledge.作者: CRAB 時(shí)間: 2025-4-1 03:15
