Discussion of representative algorithms and data structures encountered in applications. Familiarity with Prim and Kruskal minimum spanning tree algorithms and Dijkstra shortest path algorithm.

Distributed data mining techniques and mainly distributed clustering are widely used in the last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally.

Data stream clustering algorithms play a pivotal role in the analysis of rapidly accumulating and evolving data sets, particularly as technologies such as the Internet of Things expand and ...

PhoSim simulates the atmosphere, including air turbulence, as well as distortions from the shape of the telescope’s mirrors and the electrical properties of the sensors. The photons are propagated ...

Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both ...

In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient ...

The government is focusing on developing and attracting investments in priority sectors such as mining, data centers and semiconductors over the next three years as it aims to position the country ...

MUSCAT: A significant initiative to commercialise the abundant industrial minerals found in Oman’s Dhofar Governorate is advancing with the establishment of the Sultanate of Oman’s first Integrated ...

Owkin has partnered 20 healthcare institutions on a patient data mining project, ATLANTIS, across oncology, immunology/inflammation, and neurology.

In BI, AI typically combines insights from subject-matter experts and research with machine learning algorithms to identify patterns in data. The AI then begins to draw inferences based on this.