![]() ![]() Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. In this paper, we evaluate three classes of synthetic data generation approaches probabilistic models, classification-based imputation models, and generative adversarial neural networks. These characteristics pose multiple modeling challenges. By and large, medical data is high dimensional and often categorical. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. Eng.Machine learning (ML) has made a significant impact in medicine and cancer research however, its impact in these areas has been undeniably slower and more limited than in other application domains. īirant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial-temporal data. In: Rabl, T., Jacobsen, H.-A., Raghunath, N., Poess, M., Bhandarkar, M., Baru, C. Ming, Z., et al.: BDGS: a scalable big data generator suite in big data benchmarking. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. Ghazal, A., et al.: BigBench: towards an industry standard benchmark for big data analytics. Rabl, T., Frank, M., Sergieh, H.M., Kosch, H.: A data generator for cloud-scale benchmarking. Computing Science Department, University of Alberta, Edmonton, Canada T6G 2E8 Pei, Y., Zaïane, O.: A synthetic data generator for clustering and outlier analysis. Loong, B.W.L.: Topics and applications in synthetic data. Gray, J., et al.: Quickly generating billion-record synthetic databases. In: Proceedings of the 32nd International Conference on Very Large Data Bases. Houkjær, K., Torp, K., Wind, R.: Simple and realistic data generation. Nowok, B., Raab, G.M., Dibben, C.: synthpop: Bespoke creation of synthetic data in R. In: Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering. Soltana, G., Sabetzadeh, M., Briand, L.C.: Synthetic data generation for statistical testing. In: Rabl, T., Poess, M., Baru, C., Jacobsen, H.-A. Rabl, T., Jacobsen, H.-A.: Big data generation. The results of the hybrid clustering algorithm show that such artificial data can be created, which reflect the statistical properties of any given sample. After the data is clustered, the individual sub-clusters are statistically analyzed, and based on the analytical results pseudo-random data are generated. ![]() The hybrid algorithm focuses on unifying the strengths of both clustering algorithms. We introduce a hybrid clustering method, which combines both of the previously mentioned algorithms. Throughout the paper we explain how any given data can be represented numerically, and hence clustered using the DBSCAN and K-means algorithms. The scope of this paper is to describe a method for statistical data generation based on a given sample, where the generated result attempts to reflect the statistical properties of the sample as much as possible. Data augmentation can also be very useful in Big Data benchmarking tests. If the generated data is much greater in number than the given sample, then the process is called data augmentation or synthetic data generation. By generating such data, database algorithms can be stress-tested and evaluated by their performance. Due to the ever increasing data stored in databases, it is important to develop software which can generate large numbers of test data that reflect the properties of a given sample.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |