In this paper, a novel, parameter-free, incremental local density and cluster-based outlier
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …

Accepted: 20 June 2021 With k-medoids algorithm, it often takes many iterations to cluster a
large dataset, that is, the k-medoids algorithm cannot achieve the optimal performance …

In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

Spectral clustering aims to minimise inter-cluster similarity by constructing graph model,
which possesses a significant effect in data of arbitrary shape. Nonetheless, there are still …

Access plan recommendation is a query optimization approach that executes new queries
using prior created query execution plans (QEPs). The query optimizer divides the query …

Cloud computing is trusted and used by millions of users around the world. Therefore, it
becomes essential to offer trusted and efficient services to its users. Query processing and …

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult
task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic …

Digital filtering algorithms are most frequently used to implement generic-based Field-
programmable gate arrays (FPGAs) chips, which are used for higher sampling rates. In the …

Abstract Transportation Network Design Problem (TNDP) includes making the right choices
possible when deciding a collection of design criteria to develop a current transportation …

The efficiency of query processing in the Spark SQL big data processing engine is
significantly affected by execution plans and allocated resources. However, existing cost …