Immune k-means: a novel immune algorithm for data clustering and multiple-class discrimination / Michał Bereta, Tadeusz Burczyński // Prace Naukowe. Elektronika / Politechnika Warszawska. – 2006, z. ... więcej

Immune k-means: a novel immune algorithm for data clustering and multiple-class discrimination / Michał Bereta, Tadeusz Burczyński // Prace Naukowe. Elektronika / Politechnika Warszawska. – 2006, z. 156, s. 49-60 : rys., tab., wykr. – Bibliogr. 8 poz., Abstr. – Tyt. zesz.: Evolutionary Computation and Global Optimization 2006. – Mat. konf.: IX Krajowa Konferencja Algorytmy Ewolucyjne i Optymalizacja Globalna, Murzasichle, Poland, 31.05-02.06.2006. – ISSN 0137-2343

This paper presents a novel approach to data clustering and multiple-class classification problems. The proposed method is based on a metaphor derived from immune systems, the clonal selection paradigm. ... więcej

This paper presents a novel approach to data clustering and multiple-class classification problems. The proposed method is based on a metaphor derived from immune systems, the clonal selection paradigm. A novel clonal selection algorithm - Immune K-Means, is proposed. The proposed system is able to cluster real valued data efficiently and correctly, dynamically estimating the number of clusters. In classification problems discrimination among classes is based on the k-nearest neighbor method. Two different types of suppression are proposed. They enable the evolution of different populations of lymphocytes well suited to a given problem : clustering or classification. The first type of suppression enables the lymphocytes to discover the data distribution while the second type of suppression focuses the lymphocytes on the classes' boundaries. Primary results on artificial data and a real-world benchmark dataset (Fisher's Iris Database) as well as a discussion of the parameters of the algorithm are given.