An Efficient Continuous-Valued Attribute Handling Algorithm for Mining Concept-Drifting Data Streams Based on the Extended Hash Table
Tao Wang · Computer Engineering and Science · 2008
This paper focuses on continuous-valued attribute handling for mining concept-drifting data streams.Data stream is an incremental,online and real-time 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 effective algorithms for resolving the problem of concept drifting in data stream mining.Based on CVFDT,the paper proposes an efficient continuous-valued attribute 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 optimally partitioned nodes of each continuous-valued attribute.