utd-bigdata-analytics-and-management
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Name: Big Data Analytics and Management @UTDallas (Director: Dr. Latifur Khan)
Type: Organization
Bio: We focuse on data mining and machine learning algorithms for analyzing very large amounts of data
Location: Richardson, TX
Blog: www.utdallas.edu/~lkhan/BigDataLab.html
Big Data Analytics and Management @UTDallas (Director: Dr. Latifur Khan)'s Projects
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.
Efficient Multistream Classification using Direct DensIty Ratio Estimation
Multistream Classification
New York Taxi trip demand prediction using Machine learning models trained with temporal features
Efficient Sampling-based Kernel Mean Matching
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
Securing Data Analytics on Intel SGX using Randomization