Swarms on Continuous Data

Abstract

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.

6 pages, 3 figures, at http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_45.html

References in corpus (3)

• Self-Organized Stigmergic Document Maps: Environment as a Mechanism for Context Learning
• Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition
• From Feature Extraction to Classification: A multidisciplinary Approach applied to Portuguese Granites

Cited by in corpus (3)

• ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
• Evolving a Stigmergic Self-Organized Data-Mining
• Swarming around Shellfish Larvae