Similarity measures have long been utilized in information retrieval and machine learning
domains for multi-purposes including text retrieval, text clustering, text summarization …

Abstract In Distributed Database Systems (DDBS), communication costs and response time
have long been open-ended challenges. Nevertheless, when DDBS is carefully designed …

In this work, the agglomerative hierarchical clustering and K-means clustering algorithms
are implemented on small datasets. Considering that the selection of the similarity measure …

Distributed database system (DDBS) design is still an open challenge even after decades of
research, especially in a dynamic network setting. Hence, to meet the demands of high …

Data normalization is a vital preprocessing technique in which the data is either scaled or
converted so features will make an equal contribution. The success of classifiers, like K …

the k-nearest neighbors (kNN) algorithm is naturally used to search for the nearest
neighbors of a test point in a feature space. A large number of works have been developed …

In this work, a simple yet robust neighboring-aware hierarchical-based clustering approach
(NHC) is developed. NHC employs its dynamic technique to take into account the …

Effective data fragmentation is essential in enhancing the performance of distributed
database management systems (DDBMS) by strategically dividing extensive databases into …

Data allocation problem (DAP) is of great importance in distributed database systems (DDS).
Minimizing the total cost of transactions and queries is the main objective of DAP which is …

With the growth of information technology and computer networks, there is a vital need for
optimal design of distributed databases with the aim of performance improvement in terms of …