Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Abstract

Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy, that models’ the notion of adjacency that will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS.

Product Bundling Strategy for Office Supplies Retailer through Association Rules Mining: Comparative Study of Apriori and ECLAT Algorithms

“…Association rule Pattern Establishment is a data mining technique to discover relationships between variables in a dataset [14]. Specifically, it seeks to identify frequent patterns or sets of items that frequently co-occur in a dataset and use these patterns to generate association rules that capture the dependencies between the items [15]. After all the frequent item set patterns are found, the association rules that meet the minimum confidence requirements are searched by calculating the confidence value of the association rules A → B obtained from formula three below.…”

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

“…Rules. Association rules can effectively reflect the degree of association between objects in a large number of data and can mine the important information contained in the dataset, which is widely used in many fields [10].…”