Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
An architecture for tuple-based coordination of multi-agent systems
Software—Practice & Experience
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Orange: from experimental machine learning to interactive data mining
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of autonomic communications
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Application of ant K-means on clustering analysis
Computers & Mathematics with Applications
A Space-Based Generic Pattern for Self-Initiative Load Balancing Agents
ESAW '09 Proceedings of the 10th International Workshop on Engineering Societies in the Agents World X
Improving ant colony optimization algorithm for data clustering
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A coordination-based access control model for space-based computing
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agent-based nor configurable for many topologies. In this paper we propose a generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a generic architectural pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.