LNAI 2801 Discovering Clusters in Spatial Data Using Swarm Intelligence 1st Edition by Gianluigi Folino, Agostino Forestiero, Giandomenico Spezzano ISBN 9783540200574 354020057X

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Product details:
ISBN 10: 354020057X
ISBN 13: 9783540200574
Author: Gianluigi Folino, Agostino Forestiero, Giandomenico Spezzano

This paper presents a novel algorithm that uses techniques adapted from models originating from biological collective organisms to discover clusters of arbitrary shape, size and density in spatial data. The algorithm combines a smart exploratory strategy based on the movements of a flock of birds with a shared nearest-neighbor clustering algorithm to discover clusters in parallel. In the algorithm, birds are used as agents with an exploring behavior foraging for clusters. Moreover, this strategy can be used as a data reduction technique to perform approximate clustering efficiently. We have applied this algorithm on synthetic and real world data sets and we have measured, through computer simulation, the impact of the flocking search strategy on performance.

LNAI 2801 Discovering Clusters in Spatial Data Using Swarm Intelligence 1st Edition Table of contents:

Chapter 1: Introduction to Clustering and Spatial Data

• Defining Clustering and Its Applications
• Characteristics of Spatial Data
• Traditional Methods of Clustering Spatial Data
• Challenges in Clustering Spatial Data

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Chapter 2: Introduction to Swarm Intelligence

• What is Swarm Intelligence?
• Biological Inspiration: Collective Behavior in Nature
• Key Algorithms in Swarm Intelligence: Ant Colony Optimization, Particle Swarm Optimization, etc.
• The Connection Between Swarm Intelligence and Clustering

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Chapter 3: Swarm Intelligence in Data Mining

• The Role of Swarm Intelligence in Data Mining
• Advantages of Using Swarm Intelligence for Clustering
• Overview of Popular Swarm Intelligence Algorithms for Data Mining
• Applications of Swarm Intelligence in Real-World Problems

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Chapter 4: Spatial Data Mining and Its Challenges

• Overview of Spatial Data Mining
• Types of Spatial Data: Geospatial, Geometric, etc.
• Unique Challenges in Mining Spatial Data
• Approaches to Handling the Challenges of Spatial Clustering

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Chapter 5: Swarm Intelligence Approaches for Spatial Clustering

• Adapting Swarm Intelligence for Spatial Data
• Ant Colony Optimization for Spatial Clustering
• Particle Swarm Optimization in the Context of Spatial Data
• Hybrid Approaches Using Swarm Intelligence
• Case Studies of Swarm Intelligence in Spatial Clustering

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Chapter 6: Clustering Algorithms Based on Swarm Intelligence

• Overview of Specific Algorithms for Spatial Data Clustering
• Comparison of Swarm Intelligence Clustering Algorithms
• Performance Evaluation and Efficiency of Swarm-Based Algorithms
• Algorithmic Enhancements for Improved Results

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Chapter 7: Applications of Swarm Intelligence in Spatial Data Clustering

• Geographic Information Systems (GIS) and Spatial Data
• Environmental Data Mining Using Swarm Intelligence
• Clustering in Remote Sensing Data
• Applications in Urban Planning, Agriculture, and Ecology
• Case Study: Identifying Clusters in Geospatial Data for Urban Development

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Chapter 8: Experimental Setup and Results

• Design of Experiments for Swarm-Based Clustering Algorithms
• Datasets Used for Testing Spatial Clustering Algorithms
• Performance Metrics for Evaluating Clustering Results
• Experimental Results and Discussion

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Chapter 9: Improving Clustering Performance with Hybrid Approaches

• Combining Swarm Intelligence with Other Techniques
• The Role of Machine Learning in Enhancing Clustering
• Hybrid Algorithms for Handling Complex Spatial Data
• Performance Comparison of Hybrid Approaches

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Chapter 10: Future Directions and Challenges

• Emerging Trends in Swarm Intelligence and Spatial Clustering
• Challenges in Scaling Swarm Intelligence for Large Datasets
• Future Applications in Big Data and Real-Time Clustering
• Advancements in Hybrid Methods and Artificial Intelligence

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Conclusion

• Summary of Key Concepts and Findings
• Implications of Swarm Intelligence for Future Spatial Data Mining
• Final Thoughts on the Role of Swarm Intelligence in Data Mining

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