scholarly journals Design and Development of FPGA-based High-Performance Radar Data Stream Mining System

2015 ◽  
Vol 55 ◽  
pp. 876-885 ◽  
Author(s):  
Ying Liu ◽  
Pengshan Ma ◽  
Hongyuan Cui
Keyword(s):  
Data Stream ◽  
High Performance ◽  
Radar Data ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Design And Development ◽  
Mining System

scholarly journals Adaptive Ensemble with Human Memorizing Characteristics for Data Stream Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yanhuang Jiang ◽  
Qiangli Zhao ◽  
Yutong Lu
Keyword(s):  
Data Stream ◽  
Data Stream Mining ◽  
Memory Retention ◽  
Stream Mining ◽  
Mining System ◽  
Complex Concept ◽  
Knowledge Repository ◽  
Component Classifier ◽  
Concept Drifts ◽  
Forgetting Mechanism

Combining several classifiers on sequential chunks of training instances is a popular strategy for data stream mining with concept drifts. This paper introduces human recalling and forgetting mechanisms into a data stream mining system and proposes a Memorizing Based Data Stream Mining (MDSM) model. In this model, each component classifier is regarded as a piece of knowledge that a human obtains through learning some materials and has a memory retention value reflecting its usefulness in the history. The classifiers with high memory retention values are reserved in a “knowledge repository.” When a new data chunk comes, most useful classifiers will be selected (recalled) from the repository and compose the current target ensemble. Based on MDSM, we put forward a new algorithm, MAE (Memorizing Based Adaptive Ensemble), which uses Ebbinghaus forgetting curve as the forgetting mechanism and adopts ensemble pruning as the recalling mechanism. Compared with four popular data stream mining approaches on the datasets with different concept drifts, the experimental results show that MAE achieves high and stable predicting accuracy, especially for the applications with recurring or complex concept drifts. The results also prove the effectiveness of MDSM model.

MineFleet : The Vehicle Data Stream Mining System for Ubiquitous Environments

2010 ◽  
pp. 235-254 ◽  
Author(s):  
Hillol Kargupta ◽  
Michael Gilligan ◽  
Vasundhara Puttagunta ◽  
Kakali Sarkar ◽  
Martin Klein ◽  
...  
Keyword(s):  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Mining System ◽  
Vehicle Data

scholarly journals Real-Time Clinical Decision Support System with Data Stream Mining

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Zhang ◽  
Simon Fong ◽  
Jinan Fiaidhi ◽  
Sabah Mohammed
Keyword(s):  
Real Time ◽  
Support System ◽  
Data Stream ◽  
Early Years ◽  
Data Stream Mining ◽  
Diabetes Diagnosis ◽  
Stream Mining ◽  
Mining System ◽  
Data Set ◽  
Clinical Support

This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their early years and can hardly meet today's medical requirement. In this paper, we describe a clinical-support-system based data stream mining technology; the design has taken into account all the shortcomings of the existing clinical support systems.

scholarly journals A Data Stream Mining System

2008 ◽  
Author(s):  
Hetal Thakkar ◽  
Barzan Mozafari ◽  
Carlo Zaniolo
Keyword(s):  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Mining System
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