Titlebook: Data Science; Third International Beiji Zou,Min Li,Zeguang Lu Conference proceedings 2017 Springer Nature Singapore Pte Ltd. 2017 Data ana

CRUST

书目名称Data Science影响因子(影响力)




书目名称Data Science影响因子(影响力)学科排名




书目名称Data Science网络公开度




书目名称Data Science网络公开度学科排名




书目名称Data Science被引频次




书目名称Data Science被引频次学科排名




书目名称Data Science年度引用




书目名称Data Science年度引用学科排名




书目名称Data Science读者反馈




书目名称Data Science读者反馈学科排名

phase-2-enzyme

Research of Detection Algorithm for Time Series Abnormal Subsequence,rom time series data plays a very important role in data mining. In this paper, we focus on the abnormal subsequence detection. The original definition of . subsequences is defective for some kind of time series, in this paper we give a more robust definition which is based on the k nearest neighbor

悠然

An Improved SVM Based Wind Turbine Multi-fault Detection Method, was used to reduce the dimension of target features to 1-D, so that PCA output 1-D data can be used as label of support vector machine (SVM). Thus on the premise of not losing the prediction correctness, one model can detect the fault of 2 to 4 features, largely reduce the complexity of model build

小步舞

GPU Based Hash Segmentation Index for Fast T-overlap Query,fication framework, thus in low efficiency. Modern GPU has much higher parallelism as well as memory bandwidth than CPU and can be used to accelerate T-overlap query. In this paper, we use hash segmentation to divide inverted lists into segments, then design an efficient inverted index called GHSII

Collected

Gum-Disease

Gum-Disease

,Further Analysis of Candlestick Patterns’ Predictive Power,lp resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering and pattern knowledge mining to research the predictive power of candlestick patterns. In addition, we propose the similarity match model and nearest neighbor-clustering algorithm to solve the p

ethnology

开花期女

CARE

Disease Prediction Based on Transfer Learning in Individual Healthcare,present disease prediction models based on transfer learning. Breast cancer disease data has been used to build our model. According to the neural networks, the basic model has been provided. With unlabeled data, transfer learning is a appropriate way to revise the module to increase accuracy. The t