Agent Mining: The Synergy of Agents and Data Mining

2009 ◽  
Vol 24 (3) ◽  
pp. 64-72 ◽  
Author(s):  
Longbing Cao ◽  
Vladimir Gorodetsky ◽  
Pericles A. Mitkas
Keyword(s):  
Data Mining ◽  
Agent Mining

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.

scholarly journals EMPLOYING AGENTS IN DESCRIPTIVE MINING

2016 ◽  
Vol 4 (2) ◽  
pp. 111-120
Author(s):  
R. Sabitha ◽  
Karthik
Keyword(s):  
Data Mining ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Agent Technology ◽  
Distributed Environment ◽  
New Techniques ◽  
Distributed Problem Solving ◽  
Agent Mining ◽  
Intelligent Information ◽  
Different Sources

Agent technology and Data Mining have emerged as two of the prominent areas in information sciences. An effort has been activated towards the interaction and integration between agent technology and data mining which is referred to as “AGENT MINING”. Data Mining is the process of extracting interesting information or patterns from large volumes of data. Agents comprise a powerful technology for the analysis, design and implementation of autonomous intelligent systems that can handle distributed problem-solving, cooperation, coordination, communication, and organization in a multiplayer environment. This agent uses information technology to find trends and patterns in an abundance of information from many different sources. The user can sort through this information in order to find whatever information they are seeking. Intelligent agents are today accepted as powerful tools for data mining in a distributed environment. The interaction and integration between agent and mining has potential to not only strengthen either side, but generate new techniques for developing more powerful intelligence and intelligent information processing systems. This paper discusses how agents are used in the various descriptive models of Data Mining. The various challenges and methodologies are analyzed and it clearly indicates the need for and the promising potential of agent mining for the mutual enhancement of both fields and for the creation of super-intelligent systems. Even though many researchers have been committed, more efforts are required to develop techniques and systems in practical perspectives.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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