Swarms on Continuous Data

While being it extremely important, many Exploratory Data Analysis (EDA [21]) systems have the inhability to perform classification and visualization in a continuous basis or to self-organize new data-items into the older ones (evenmore into new labels if necessary), which can be crucial in KDD Knowledge Discovery [10,1], Retrieval and Data Mining Systems [15,10] (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps [4,22]. On the present work, and exploiting past sucesses in recently proposed Stigmergic Ant Systems [16,17] 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 demonstraded by other authors in different areas [9,2].