Abstract
The traditional data mining algorithm focuses too much on a single dimension of data time or space, ignoring the association between time and space, which leads to a large amount of computation and low processing efficiency of the mining algorithm and makes it difficult to guarantee the final data mining effect. In response to the above problems, a hierarchical mining algorithm based on association rules for high-dimensional spatio-temporal big data is proposed. Based on the traditional association rules, after establishing the association rules of spatio-temporal data, the data to be mined are cleaned for redundancy. After selecting the local linear embedding algorithm to reduce the dimensionality of the data, a hierarchical mining strategy is developed to realize high-dimensional spatio-temporal big data mining by searching frequent predicates to form a spatio-temporal transaction database. The simulation experiment results verify that the algorithm has high complexity and can effectively reduce the processing volume, which can improve the processing efficiency by at least 56.26% compared with other algorithms.
Highlights
The essential function of spatio-temporal data is to reflect the quantitative and qualitative characteristics, spatial structure and spatial relationships of the elements or phenomena in space-time and their changes over time, which is the basis for human cognition of the geographic world[1,2]
With regard to the above analysis, in order to improve the efficiency of highdimensional spatio-temporal big data mining and reduce the data mining process, this paper will study the association rule-based hierarchical mining algorithm for high-dimensional spatio-temporal big data
In data mining using association rules, it is necessary to set the minimum support and minimum confidence according to the size of the mining object and the needs of the mining purpose[6]
Summary
Spatio-temporal data contains a large amount of data information, and the performance of data mining algorithms will directly determine the use of effective information in spatio-temporal data. Regarding the limitations of traditional data mining methods in dealing with high-dimensional data, a hierarchical mining algorithm based on association rules for highdimensional spatio-temporal big data is proposed in the above paper. The performance of this data mining algorithm will be tested by simulation experiments
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