Swarms on Continuous Data

TL;DR

This paper introduces a robust online classifier based on Stigmergic Ant Systems that can perform continuous classification and visualization of streaming data, improving upon existing methods like Self-Organizing Maps.

Contribution

It presents a novel online classification approach using swarm intelligence, enabling continuous data mapping and classification in data mining and exploratory analysis.

Findings

Achieves progressively better classification results over time

Enables continuous mapping of streaming data

Outperforms some existing approaches in online classification

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.