关键词: 数据流挖掘 CVFDT连续属性 概念漂移 扩展哈希表

Abstract:

This paper focuses on continuous-valued attribute handling for mining concept-drifting data streams. Data stream is an incremental,online and real-tim  e model. VFDT is one of the most successful algorithms in data stream mining when data take on a state of stable distribution;CVFDT is one of the effect  ive algorithms for resolving the problem of concept drifting in data stream mining. Based on CVFDT, the paper proposes an efficient continuous-valued at tribute handling method named Hash CVFDT for mining concept-drifting data streams based on the extended hash table. The algorithm is as fast as the hash  table in attribute inserting, seeking and deleting, and solves the flaws of the hash table which cannot output. Sequently when selecting the opthnally   partitioned nodes of each continuous-valued attribute.

Key words: data streaming, CVFDT, continuous-valued attribute, concept drifting, extended hash table