Introduction and Overview

• Chen, C. (2010). Information visualization , Wiley Interdisciplinary Review: Computational Statistics, 2(4)(July/August), 387-403.
Recent introductory overview of information visualization that does a fine job of presenting the essential nature of the field in a comprehensive, yet accessible, manner.
• Schneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations , IEEE Visual Languages, 336-343.
Among the earliest and most cited explications of the then emerging field of information visualization in which Schneidermand presents a "Visual Information Seeking Mantra": "Overview first, zoom and filter, then details-on-demand."
• Card, S.K., Mackinlay, J.D. & Shneiderman, B. (1999). Using Vision to Think: Readings in Information Visualization , Morgan Kaufman.
Card et al.'s book provides a collection of seminal papers in information visualization. It has been made available as a "preview" (with the odd missing pages) through Google Books. Chapter 1 provides an overview of information visualization and its utility in cognitive tasks.
• Reynolds, C. (1987). Flocks, Herds, and Schools: A Distributed Behavioral Model , Computer Graphics, 21(4), 25-34.
Reynold's brief, seminal work on simulating the behavior of groups of animals, e.g., birds, for computer animation.
• Reynolds, C. (1999). Steering Behaviors for Autonomous Characters , Proceedings of Game Developers Conference, 763–782.
Essentially, an expanded version of the previous work providing background literature, as well as detailing implementation issues and simulated behaviors in addition to flocking, e.g., path following, pursuit. The html version here is from Reynold's personal site and includes links to a number of vintage papers. In a few words about himself Reynolds explains, "For many years after I left school, almost all I did was hack on computer animation. All my friends said "Craig, get a life!" I thought they said alife and so began to study the field of Artificial Life."
• Macal, C. M. & North, M. J. (2010). Tutorial on Agent-based Modeling and Simulation , Journal of Simulation, 4, 151-162.
General overview of the approach with references across a wide range of domains.
• Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data Clustering: A Review , ACM Computing Surveys, 31(3), 264-323.
Often cited, comprehensive, and reasonably accessible review and reference for clustering techniques.
• Kennedy, J. & Eberhart, R. C. (1995). Data Clustering using Particle Swarm Optimization , Proceedings of the IEEE International Joint Conference on Neural Networks, 4, 1942–1948.
Seminal paper in which "A concept for the optimization of nonlinear functions using particle swarm methodology is introduced" that was inspired by, and draws from, the techniques of Reynolds' and others' simulations of bird flocking.
• Alec Banks, Jonathan Vincent, and Chukwudi Anyakoha (2007). A review of particle swarm optimization. Part I: Background and development , Natural Computing, 6:467–484.
This review's section "PSO: from animation to optimization" also describes the history of biologically inspired computation and Reynold's boids.
• Merwe, V. D. & Engelbrecht, A. D. (2003). Data Clustering using Particle Swarm Optimization , Proceedings of IEEE Congress on Evolutionary Computation, 215-220.
Compares k-means clustering and particle swarm optimization (PSO, or, agent-based "flocking") using several data sets. PSO was found to have a number of advantages, especially with respect to speed of convergence to a solution.
• N. Greffard, F. Picarougne, and P. Kuntz (2012). Visual community detection: An evaluation of 2D, 3D perspective and 3D stereoscopic displays , Proceedings of 19th International Symposium on Graph Drawing, Lecture Notes in Computer Science, vol. 7034, pp. 215-225.
• E. Sklar, C Jansen, J. Chan, and M. Byrd (2011). Toward a methodology for agent-based data mining and visualization , International Workshop on Agents and Data Mining Interaction (ADMI 2011), pp. 20-31.
• Weiss, R. M. (2013). Accelerating Swarm Intelligence Algorithms with GPU-Computing , GPU Solutions to Multi-scale Problems in Science and Engineering Lecture Notes in Earth System Sciences, 503-515.

Document Visualization, Clustering, and Retrieval Systems using Agent-based, e.g., Flocking, Techniques

• Bin, W., Yi, Z., Shaohui, L. & Zhongzhi, S. (2002). CSIM: A Document Clustering Algorithm Based On Swarm Intelligence , IEEE Evolutionary Computation, 2002 (CEC'02), Proceedings of the 2002 Congress on (Volume 1), 477-482.
Among the earliest applications of "swarm-based" clustering to document sets, in which an adaptation of ant colony optimization (ACO) is used. A number of variants of the algorithm appeared around this time, e.g., fuzzy ants, and through the late 2000's, many in Chinese and in the data mining literature.
• Cui, X., Potok, T. E., & Palathingal, P. (2005, June). Document clustering using particle swarm optimization , Swarm Intelligence Symposium, 2005 (SIS 2005) Proceedings 2005 IEEE (pp. 185-191).
Though visualization techniques are not explored, this is the first in a set of work by Cui, Potok, and colleagues exploring the use swarm-based techniques to the problem of document clustering. The authors describe it as perhaps the first effort to apply PSO for document collection clustering, or organization, and a hybrid PSO k-means algorithim is explored. Their later work they describe as "flock-based" and does provide examples and systems utilizing document collection visualization.
• X. Cui, J. Gao, and T. E. Potok (2006). A flocking based algorithm for document clustering analysis Journal of Systems Architecture, vol. 52, no. 8-9, pp. 505-515, 2006.
• X. Cui and T. E. Potok (2006). A distributed agent implementation of multiple species flocking model for document partition clustering , CIA 2006, Lecture Notes in Computer Science, vol. 4149, pp. 124-137, 2006.
• X. Cui and T. E. Potok (2009). Swarm Intelligence in Text Document Clustering, in Handbook of Research on Text and Web Mining Technologies, Song, M. & Wu, Y. B. (Eds.), IGI Global, 165 - 180.
• F. Picarougne, H. Azzag, G. Venturini, and C. Guinot (2007). A new approach of data clustering using a flock of agents Evolutionary Computation, vol. 15, no. 3, pp. 345-367.
A wide range of applications is investigated, and includes commentary on Cui et al.'s work.
• F. Picarougne, H. Azzag, G. Venturini, and C. Guinot (2004). Picarougne_2004_DataClusteringFlockArtificialAgents_Tools-w-AI Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004) , pp. 777-778.
• Y. Zhang, F. Mueller, X. Cui, and T. Potok (2011). Data-intensive document clustering on graphics processing unit (GPU) clusters , Journal of Parallel and Distributed Computing, vol. 71, pp. 211-224.
• Fowler, R. H., Huerta, R. A., & Fowler, W. A. L. (2012). Visualization and Clustering of Document Collections using a Flock-based Swarm Intelligence Technique , Proceedings of Modeling, Simulation and Visualization (MSV'12), 143-148.
• Milam, D. & Pasquier, P. (2013). Boidz: An ALife Augmented Reality Ambient Visualization , "http://www.researchgate.net/publication/228817932_Boidz_An_ALife_Augmented_Reality_Ambient_Visualization

Other Systems using Agent-Based Techniques for Data, etc., Analysis and Display

• G. Folino and G. Spezzano (2002). An adaptive flocking algorithm for spatial clustering , Parallel Problem Solving in Nature (PPSN) VII, Lecture Notes in Computer Science, vol. 2439, pp. 924-933.
• A.V. Moere (2004). Information flocking: Time-varying data visualization using Boid behaviors , Proceedings of the Eighth International Conference on Information Visualization, pp. 409–414.
• S. Momen, B. P. Amavasai, and N. H. Siddique (2007). Mixed Species Flocking for Heterogeneous Robotic Swarm EUROCON 2007 The International Conference on Computer as a Tool, pp. 2329-2336, 2007.
• F. Picarougne, H. Azzag, G. Venturini, and C. Guinot (2004). On data clustering with a flock of artificial agents , Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), pp. 777-778.
• F. Picarougne, H. Azzag, G. Venturini, and C. Guinot (2007). A new approach of data clustering using a flock of agents Evolutionary Computation, vol. 15, no. 3, pp. 345-367.
• G. Proctor and C. Winter (1998). Information flocking: Data visualisation in virtual worlds using emergent behaviours , Proceedings of Virtual Worlds, pp. 168–176.