Adaptive Data Mining Algorithms for Non-Stationary Data

非平稳数据的自适应数据挖掘算法

• 批准号: RGPIN-2015-05224
• 负责人: Kamel, Mohamed
• 金额: $ 4.37万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590294
• 关键词: Adaptive; Data; Mining; Algorithms; Non

Today's information and communication systems are characterized by the continuous generation, transfer and storage of massive amounts of data. Most of these data typically evolve over time and their properties change from one domain to another. Although different data mining techniques have been proposed for analyzing large-scale data, this non-stationary nature of the data negatively affects the sustainability of these techniques over time and prevents their applicability to new domains. For instance, systems for product recommendations cannot quickly adapt to the ever-changing needs of the customers. These shortcomings motivate the research and development of data mining algorithms that can quickly adapt to changes in non-stationary data.

今天的信息和通信系统的特点是不断产生、传输和存储大量数据。这些数据中的大多数通常会随着时间的推移而演变，并且它们的属性会从一个域到另一个域发生变化。虽然已经提出了不同的数据挖掘技术来分析大规模数据，但数据的这种非平稳性质会随着时间的推移对这些技术的可持续性产生负面影响，并阻止其适用于新的领域。例如，产品推荐系统无法快速适应客户不断变化的需求。这些缺点促使研究和开发能够快速适应非平稳数据变化的数据挖掘算法。