Figure – From top left to bottom right, a sequential data-items clustering task performed by an artificial ant colony. The system is able to cope with unforeseen data items in real-time, that is, as data appears in a continuous basis over a large period of time. Also, as time evolves, spatial entropy decreases.
 Vitorino Ramos, Ajith Abraham, Swarms on Continuous Data, in CEC´03 – Congress on Evolutionary Computation, IEEE Press, ISBN 078-0378-04-0, pp.1370-1375, Canberra, Australia, 8-12 Dec. 2003.
While being it extremely important, many Exploratory Data Analysis (EDA) systems have the inability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (even more into new labels if necessary), which can be crucial in KDD – Knowledge Discovery, Retrieval and Data Mining Systems (interactive and online forms of Web Applications are just one example). This disadvantage is also present in more recent approaches using Self-Organizing Maps. On the present work, and exploiting past successes in recently proposed Stigmergic Ant Systems a robust online classifier is presented, which produces class decisions on a continuous stream data, allowing for continuous mappings. Results show that increasingly better results are achieved, as demonstrated by other authors in different areas.
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