Name
Title | ||
|---|---|---|
Deep Data Anaylizing Application Based on Scale Space Theory in Big Data Environment. |
Abstract | ||
|---|---|---|
This paper introduces the basic scientific idea of the multi-scale to the field of big data analyzes, proposes a multi-scale framework of data analyzes in big data environment, present the multi-scale algorithm framework of knowledge conversion theory and apply the algorithm framework to the multi dimension association rules analysis. The proposed multi-scale association rule analysis algorithm uses the benchmark data set of analyzing results and the influence weight of benchmark data sets for target scale data sets to derived the association rules behind object scale data set, realize knowledge across scales derived and provide the possibility for multi-scale decision. Keywords-Scale Space; Big Data; Frequent Item-set; |
Year | Venue | Field |
|---|---|---|
2016 | SEKE | Data mining,Data set,Computer science,Scale space,Association rule learning,Big data |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
18 | 3 | |