Modern Very Fast Decision Tree Model for Mining High-Speed Time-Series Data Stream - Vanitha Katherine, A.; Kamalavalli, T.; Vinothini, S.; Jagannath, M.; E. Jayanthi, V. (International Journal of Engineering and Technology, 2018)
Data mining is one of the drastically growing research fields in data analysis. Data is generated on a person, object, element, label in terms of time, days, months, years. Although ample algorithms currently exist for high-speed data streams, they fail to efficiently scale up the data when the data size is large. In this paper, an algorithm is proposed to perform clustering for high-speed data streams using Modern Very Fast Decision Tree (MVFDT) model. It replaces the old decision tree model by clustering to enhance its accuracy. MVFDT takes clusters based records in the database and compares with other cluster of records if any relationship among the records. MVFDT reads a model for clustering which is similar in accuracy of Very Fast Decision Tree (VFDT). In VFDT, new samples are arrived every time for a moving window. But the result of VFDT does not provide satisfactory in terms of data scalability, i.e., large in volume. MVFDT incorporates three different functionalities such as dynamic tree formation, windowing based clustering and classification for calculating the Frequent Pattern (FP) and query process. Experiments are carried out by using large set of time-series and time-changing data streams to compare the clustering and mining efficiency of MVFDT. Experiment results seem to prove that MVFDT model provides more mining efficiency than VFDT.Â Â