连续数据上的群集
Swarms on Continuous Data
摘要：
虽然非常重要，但许多探索性数据分析（EDA）系统无法连续地执行分类和可视化，或者将新的数据项自组织到旧的数据项中（如果必要的话，甚至到新的标签中），这在KDD -知识发现，检索和数据采掘系统（交互式和在线形式的Web应用程序只是一个例子）中可能是至关重要的。这种现象也出现在使用自组织映射的最近方法中。在目前的工作，并利用过去的成功，最近提出的Stigmergic蚂蚁系统的强大的在线分类器，产生类的决定，连续流数据，允许连续映射。结果表明，越来越好的结果，取得了其他作者在不同领域的示范。关键词：群体智能，蚂蚁系统，Stigmergy，数据挖掘，探索性数据分析，图像检索，连续分类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
