Data stream is a potentially massive, continuous, rapid sequence of data information. It has
aroused great concern and research upsurge in the field of data mining. Clustering is an …

Abstract Concept drift is a common and important issue in streaming data analysis and
mining. Thus far, many concept drift detection methods have been proposed but may not be …

Tracking recurring concept drifts is a significant issue for machine learning and data mining
that frequently appears in real-world stream classification problems. It is a challenge for …

Streaming data has become an important form in the era of big data, and the concept drift, as
one of the most important problem of it, is often studied deeply. However, similar to true …

Text data streams have widely appeared in real-world applications, in which, concept drifts
owe a significant challenge for classification. Compared with relational data streams …

Incremental learning techniques have been used extensively to address the data stream
classification problem. The most important issue is to maintain a balance between accuracy …

摘要大数据面向异构自治的多源海量数据, 旨在挖掘数据间复杂且演化的关联.
随着数据采集存储和互联网技术的发展, 大数据分析和应用已成为各行各业的研发热点 …

A common assumption in machine learning is that training data is complete, and the data
distribution is fixed. However, in many practical applications, this assumption does not hold …

Abstract Concept drift detection and processing is an important issue in streaming data
mining. When concept drift occurs, online learning model often cannot quickly adapt to the …

摘要针对现有算法聚类精度不高, 处理离群点能力较差以及不能实时检测数据流变化的缺陷,
提出一种基于密度与近邻传播融合的数据流聚类算法. 该算法采用在线/离线两阶段处理框架 …