Swarms on Continuous Data
<|reference_start|>Swarms on Continuous Data: While being it extremely important, many Exploratory Data Analysis (EDA) 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, Retrieval and Data Mining Systems (interactive and online forms of Web Applications are just one example). This disadvantge is also present in more recent approaches using Self-Organizing Maps. On the present work, and exploiting past sucesses 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 demonstraded by other authors in different areas. KEYWORDS: Swarm Intelligence, Ant Systems, Stigmergy, Data-Mining, Exploratory Data Analysis, Image Retrieval, Continuous Classification.<|reference_end|>
@article{ramos2004swarms,
    title={Swarms on Continuous Data},
    author={Vitorino Ramos, Ajith Abraham},
    journal={CEC 03 - Congress on Evolutionary Computation, IEEE Press, ISBN
  0780378040, pp.1370-1375, Canberra, Australia, 8-12 Dec. 2003},
    year={2004},
    doi={10.1109/CEC.2003.1299828},
    archivePrefix={arXiv},
    eprint={cs/0412072},
    primaryClass={cs.AI cs.NE}
}