A Multi-agent and Data Mining Model for TCM Cases Knowledge Discovery

2008 ◽  
Author(s):  
Zhengxiang Zhu ◽  
Wuqi Song ◽  
Jifa Gu
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Multi Agent ◽  
Mining Model

Agent mining approaches: an ontological view

2021 ◽  
Vol 36 ◽  
Author(s):  
Emmanuelle Grislin-Le Strugeon ◽  
Kathia Marcal de Oliveira ◽  
Dorsaf Zekri ◽  
Marie Thilliez
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Knowledge Discovery ◽  
Systematic Literature Review ◽  
Software Agent ◽  
Application Domain ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Agent Mining ◽  
Multi Agent

Abstract Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

Data Mining and Knowledge Discovery

2010 ◽  
pp. 84-110
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Intelligent Manufacturing ◽  
Support Vector ◽  
Knowledge Based ◽  
Database Technology ◽  
Multi Agent ◽  
Basic Functions ◽  
Visualization Technology ◽  
Clustering Data

In Chapters 2 and 3, the knowledge-based system and Multi-Agent system were illustrated. These are significant methods and theories of Manufacturing Intelligence (MI). Data Mining (DM) and Knowledge Discovery (KD) are at the foundation of MI. Humans are immersed in data, but are thirsty for knowledge. With the wider application of database technology, a dilemma has arisen whereby people are ‘rich in data, poor in knowledge’. The explosion of knowledge and information has brought great benefit to mankind, but has also carried with it certain drawbacks, since it has resulted in knowledge and information ‘pollution. Facing a vast but polluted ocean of data, a technical means to discard the bad and retain the good was sought. Data Mining and Knowledge Discovery (DMKD) was therefore proposed against the background of rapidly expanding data and databases. It is also the result of the development and fusion of database technology, Artificial Intelligence (AI), statistical techniques and visualization technology (Fayyad U., 1998). DMKD has become a research focus and cutting-edge technology in the field of computer information processing (Jef Woksem, 2001). The development background, conception, working process, classification and general application of DM and KD are firstly introduced in this chapter. Secondly, basic functions and assignment such as prediction, description, data clustering, data classification, conception description and visualization processing are discussed. Then the methods and tools for DM are presented, such as the association rule, decision tree, genetic algorithm, rough set and support vector machine. Finally, the application of DMKD in intelligent manufacturing is summarized.

Ontology-based data mining model management for self-service knowledge discovery

2016 ◽  
Vol 19 (4) ◽  
pp. 925-943 ◽  
Author(s):  
Yan Li ◽  
Manoj A. Thomas ◽  
Kweku-Muata Osei-Bryson
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Model Management ◽  
Self Service ◽  
Mining Model
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