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Agents and Data Mining Interaction

Overview of attention for book
Cover of 'Agents and Data Mining Interaction'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Intersections of the Future: Using Fully Autonomous Vehicles
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    Chapter 2 Toward a Methodology for Agent-Based Data Mining and Visualization
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    Chapter 3 A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents
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    Chapter 4 Agent Enriched Distributed Association Rules Mining: A Review
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    Chapter 5 Obtaining an Optimal MAS Configuration for Agent-Enhanced Mining Using Constraint Optimization
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    Chapter 6 Towards a Numerical, Agent-Based, Behaviour Analysis: The Case of Tourism
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    Chapter 7 A Comparative Study of a Financial Agent Based Simulator Across Learning Scenarios
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    Chapter 8 Agent-Based Cluster Analysis of Tropical Cyclone Tracks in the Western North Pacific
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    Chapter 9 Exploiting Domain Knowledge in Making Delegation Decisions
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    Chapter 10 Data Mining to Support Human-Machine Dialogue for Autonomous Agents
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    Chapter 11 An Instance-Window Based Classification Algorithm for Handling Gradual Concept Drifts
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    Chapter 12 Towards a Multiagent-Based Distributed Intrusion Detection System Using Data Mining Approaches
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    Chapter 13 Change Point Analysis for Intelligent Agents in City Traffic
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    Chapter 14 Mining Frequent Agent Action Patterns for Effective Multi-agent-Based Web Service Composition
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    Chapter 15 Enhancing Agent Intelligence through Evolving Reservoir Networks for Predictions in Power Stock Markets
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    Chapter 16 Pricing Analysis in Online Auctions Using Clustering and Regression Tree Approach
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    Chapter 17 The Impact of Recommender Systems on Item-, User-, and Rating-Diversity
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    Chapter 18 Opinion Formation in the Social Web: Agent-Based Simulations of Opinion Convergence and Divergence
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    Chapter 19 A Data-Driven Approach for Resource Gathering in Real-Time Strategy Games
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    Chapter 20 Data Cloud for Distributed Data Mining via Pipelined MapReduce
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    Chapter 21 Successful Efficient and Intelligent Retrieval Using Analytic Hierarchy Process
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    Chapter 22 A Hybrid System Based on Multi-Agent Systems in Case of e-WeddingThailand
Overall attention for this book and its chapters
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

1 tweeter


1 Dimensions

Readers on

8 Mendeley
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Agents and Data Mining Interaction
Published by
Lecture notes in computer science, January 2012
DOI 10.1007/978-3-642-27609-5
978-3-64-227608-8, 978-3-64-227609-5

Cao, Longbing, Bazzan, Ana L. C., Symeonidis, Andreas L., Gorodetsky, Vladimir I., Weiss, Gerhard, Yu, Philip S.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Greece 1 13%
Unknown 7 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 25%
Student > Ph. D. Student 1 13%
Unknown 5 63%
Readers by discipline Count As %
Computer Science 3 38%
Unknown 5 63%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 August 2012.
All research outputs
of 13,609,947 outputs
Outputs from Lecture notes in computer science
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Outputs of similar age
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Outputs of similar age from Lecture notes in computer science
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Altmetric has tracked 13,609,947 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,410 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,718 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.