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

征兵

A Collaborative Filtering Recommendation Algorithm Based on the Difference and the Correlation of U to solve the problem that the algorithm is not accurate in spare dataset, we improve it by prefilling the vacancy of rating matrix. Experiment results show that this algorithm improves significantly the accuracy of the recommendation after prefilling the rating matrix.

過(guò)分

Partial Least Squares (PLS) Methods for Abnormal Detection of Breast Cells, we used the 10 features of breast cells to predict it, the results reaching upto 93.67% accuracy, it was very effective to predict and analyse whether the breast cells getting cancer, It had an important role in the diagnosis and prevention of breast cancer.

confide

Research on Fuzzy Matching Query Algorithm Based on Spatial Multi-keyword,to this algorithm, which not only supports multiple POI queries, but also supports fault tolerance of the query keywords. The simulation results demonstrate that the proposed algorithm can improve the accuracy and efficiency of query.

Flatus

Research on XDR Bill Compression Under Big Data Technology,ed method in practice. It solves the problem of occupying large storage and low analysis efficiency in areas like the storage of the massive XDR bill, pretreatment, aggregation. It provides valuable references for telecommunication-related researchers and engineering practitioner in respect of using the big data technology.

有幫助

Composite Graph Publication Considering Important Data,nce availability is whether the important data keep original value in a composite graph. We analysis the properties of important data of k triangle count, and provide a new method for synthesis graph publication. We show the application of this method in k triangle count, and the experimental results proved the accuracy of the method.

CHIP

飛行員

FLAGR

Disease Prediction Based on Transfer Learning in Individual Healthcare,works, the basic model has been provided. With unlabeled data, transfer learning is a appropriate way to revise the module to increase accuracy. The test results show that the algorithm is suitable for data classification, especially for unlabeled health data.

北極人

Nibble