Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Data clustering with a neuro-immune network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A novel clustering algorithm based on immune network with limited resource
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Adaptive radius immune algorithm for data clustering
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Information Sciences: an International Journal
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Information Sciences: an International Journal
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Trend discovery in financial time series data using a case based fuzzy decision tree
Expert Systems with Applications: An International Journal
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
A T-cell algorithm for solving dynamic optimization problems
Information Sciences: an International Journal
A negative selection approach to intrusion detection
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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During the last decade artificial immune systems have drawn much of the researchers' attention. All the work that has been done allowed to develop many interesting algorithms which come in useful when solving engineering problems such as data mining and analysis, anomaly detection and many others. Being constantly developed and improved, the algorithms based on immune metaphors have some limitations, though. In this paper we elaborate on the concept of a novel artificial immune algorithm by considering the possibility of combining the clonal selection principle and the well known K-means algorithm. This novel approach and a new way of performing suppression (based on the usefulness of the evolving lymphocytes) in clonal selection result in a very effective and stable immune algorithm for both unsupervised and supervised learning. Further improvements to the cluster analysis by means of the proposed algorithm, immune K-means, are introduced. Different methods for clusters construction are compared, together with multi-point cluster validity index and a novel strategy based on minimal spanning tree (mst) and a analysis of the midpoints of the edges of the (mst). Interesting and useful improvements of the proposed approach by means of negative selection algorithms are proposed and discussed.