Association Rules, Spatio-temporal

Abstract. Along with the rapid development of Internet technology, GNSS technology and mobile terminals, a large amount of information including geographical location and time attributes has been generated. Faced with large and complex Internet geospatial data, how to quickly and accurately extract valuable reference information becomes an urgent problem to be solved. And the user's demand for personalized information of recommendation information is getting stronger and stronger, and researching efficient and accurate personalized recommendation system has good application value. In this paper, based on the application requirements of personalized recommendation information, the GIS platform and related recommendation algorithms are used to fully exploit the user and location based on geographic space-time big dataIt is divided into user explicit interest and user implicit interest, and then establishes a scientific and efficient user behavior motivation prediction model based on geographic situation. User interest information can be obtained from explicit interest information, implicit interest information and geographic situation interest information. Geographical environment, geographic location and other related context information. By introducing time factors, it is used to update and improve the user real-time interest model to achieve accurate prediction of user behavior motives under geographic spatio-temporal big data. Use Apriori algorithm to calculate the support and determine the current Frequent itemsets of user interest in geographic context, using frequent itemsets to generate strong association rules, and realizing the analysis of user behavior motives based on geography context. For geographic spatio-temporal big data, this paper proposes a personalized hybrid recommendation algorithm, which is based on users. Effective combination of collaborative filtering algorithms and association rules for geographic context-user behavioral interest adaptation.

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Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules

E3S Web of Conferences ◽

10.1051/e3sconf/202125602040 ◽

2021 ◽

Vol 256 ◽

pp. 02040

Author(s):

Chunlei Zhou ◽

Xinwei Dong ◽

Liang Ji ◽

Bijun Zhang ◽

Zhongping Xu ◽

...

Keyword(s):

Data Mining ◽

Big Data ◽

Association Rules ◽

High Dimensional ◽

Data Mining Algorithm ◽

Temporal Data ◽

Processing Efficiency ◽

Local Linear Embedding ◽

Mining Algorithm ◽

Spatio Temporal

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.

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