Improved fuzzy weighted‐iterative association rule based ontology postprocessing in data mining for query recommendation applications

Abstract

The usage of association rules is playing a vital role in the field of knowledge data discovery. Numerous rules have to be processed and plot based on the ranges on the schema. The step in this process depends on the user's queries. Previously, several projects have been proposed to reduce work and improve filtration processes. However, they have some limitations in preprocessing time and filtration rate. In this article, an improved fuzzy weighted‐iterative concept is introduced to overcome the limitation based on the user request and visualization of discovering rules. The initial step includes the mix of client learning with posthandling to use the semantics. The above advance was trailed by surrounding rule schemas to fulfill and anticipate unpredictable guidelines dependent on client desires. Preparing the above developments can be imagined by the use of yet another clever method of study. Standards on guidelines are recognized by the average learning professionals.

Retracted: QGMS: A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence

“…The performance of the proposed QGMS framework is baselined with other models for query recommendation namely the IWFIAR, 27 PQRA, 26 QRM, 25 QRESSG, 34 RSR, 18 QuantQueryEXP, 28 QRMExpS 20 for a set of two datasets namely the CHiC dataset and the Spring 2006 Query Logs dataset. The reason for choosing an array of baseline strategies is to bring out the importance of semantically driven strategies using ontologies in the context of query recommendation for a successful semantic search.…”

“…Apart from the genetic algorithm, the strategy intelligently integrates compound probability, Jaccard index for clicks and words separately, and the ratio of length of longest common subsequence to the maximum length of the queries. Sumathi and Akilandeswari 27 have proposed a weighted fuzzy iterative technique with association rules R e t r a c t e d for domain-specific ontologies to recommend queries effectively. Kaushik et al 28 have incorporated a Deutsch-Jozsa algorithm in an altered manner for query recommendation by giving it a computational flavor using quantum principles.…”

Enhancing data-driven elderly appointment services in domestic care communities under COVID-19

“…To extract the relevant knowledge from the huge datasets, association rule mining (ARM) is classified as a promising technology for data prediction analysis (Ţăranu, 2016; Altaf et al , 2017; Sarno et al , 2020). ARM is one of the commonest and most effective techniques for exploring the recurring relationships in data through frequent pattern mining (Sumathi and Akilandeswari, 2020). The objective of association rule mining is to discover essential knowledge from databases and identify the associations among the items in the datasets.…”

Assisting Users in Decisions Using Fuzzy Ontologies: Application in the Wine Market

“…In [37], FOs are used to define the necessary semantic for controlling smart homes using the internet of Things. In [38], Sumathi et al propose an ontology that stores information about an information processing technique for query recommendation applications. It can be concluded that FO is a quite recent field that is evolving and changing due to the research made on the area.…”