Fundamentals of Digital Technologies
Fundamentals of Digital Technologies – Group Project and Individual Refinement
This assessment is a project-based assignment for the DG4FDTL Fundamentals of Digital Technologies module. It is designed to develop students' practical understanding of digital technologies by combining mathematical concepts, programming, algorithms, data analysis and learning methods within a real-world project. The project is structured around a common group component, known as the Group Trunk, and an individual component in which each student develops a specialised refinement of the shared project. The project topics are designed around key areas covered by the module, including linear algebra, calculus, probability, learning algorithms, algorithms and Python programming. Students work in groups to establish a common project foundation and then develop an individual refinement that extends the functionality or analytical capabilities of the shared system. The project guidance is intended for a mixed cohort that may include Data Science, Computer Science and Business Analytics students. The assessment is divided into two main components: Proposal and Implementation. The Proposal accounts for 40% of the assessment, while Implementation accounts for 60%. Both the group trunk and individual trunk contribute to the assessment. The group proposal requires students to describe what the group intends to build, explain the problem being addressed, outline the proposed approach and identify the main functions and responsibilities within the project. Students should demonstrate a clear understanding of the project objectives and provide an appropriate plan for developing the shared system. The implementation stage requires students to develop the common group functionality and then complete their individual refinement. The shared component provides the basic project framework, while the individual refinement allows each student to investigate a specific aspect of the problem and add specialised functionality. Depending on the selected project topic, individual refinements may involve data analysis, visualisation, optimisation, prediction, monitoring, reporting or other computational features. The project topics include practical applications such as productivity and task-management systems, supermarket sales analysis and other data-driven applications. Students are expected to use Python and appropriate libraries or computational techniques to implement their solutions. The project materials provide examples involving data structures, CSV files, functions, numerical calculations, visualisation and analytical dashboards. The assessment emphasises both technical implementation and the student's ability to explain the problem, approach and functionality of the developed system. Students should demonstrate appropriate use of programming concepts, mathematical foundations, algorithms and data-analysis techniques. The individual refinement should clearly extend the common project and demonstrate the student's own contribution to the overall solution. Overall, the assessment develops practical digital-technology skills through collaborative project development followed by individual technical refinement. It provides experience in project planning, programming, computational problem solving, data analysis, visualisation and the application of mathematical and algorithmic concepts to practical problems.
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Computer Science / Algorithms / Parallel Computing / Clustering
Parallel Algorithms for Hierarchical Clustering: Single-Link, Minimum Spanning Trees and Parallel Architectures
This research paper investigates parallel algorithms for hierarchical clustering, a clustering technique in which individual data points initially form separate clusters and the closest clusters are repeatedly merged until a hierarchical tree structure, or dendrogram, is formed. The paper reviews important sequential clustering algorithms, surveys previous parallel approaches and proposes parallel methods for several commonly used inter-cluster distance metrics. Prims_algorithm_for_hierarchica… The paper distinguishes between graph-based metrics and geometric metrics. Graph metrics include single-link, average-link and complete-link clustering, while geometric metrics include centroid, median and minimum-variance methods. It also discusses the Lance–Williams updating formula, which provides a general framework for updating inter-cluster distances after agglomeration. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… A major focus is the relationship between single-link hierarchical clustering and the Euclidean minimum spanning tree. The paper explains that the cluster hierarchy for single-link clustering can be obtained from a minimum spanning tree, making minimum-spanning-tree algorithms highly relevant to efficient hierarchical clustering. It presents practical single-link algorithms with O(n²) time complexity and discusses space requirements and nearest-neighbour update properties. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… The paper also examines algorithms for metrics satisfying the reducibility property, where nearest-neighbour chains can be used to efficiently determine which clusters to merge. Minimum-variance and graph-based metrics satisfy this property, while centroid and median metrics do not necessarily do so. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… For more general clustering metrics, the paper describes priority-queue-based algorithms with O(n² log n) sequential time complexity. It then reviews previous parallel work, including parallel implementations of SLINK, Ward’s method and Prim’s minimum spanning tree algorithm. The cited parallel Prim implementation achieves O(n log n) time when sufficient processors are available. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… The core contribution is a set of parallel algorithms for hierarchical clustering on PRAM, butterfly and tree architectures. For single-link clustering, the paper shows how a parallel minimum-spanning-tree approach can be used and reports an O(n log n) running time using n/log n processors. Similar optimal results are described for centroid, median and minimum-variance clustering, while average-link and complete-link methods are more difficult to optimise on local-memory architectures. Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… Prims_algorithm_for_hierarchica… Overall, the paper demonstrates how hierarchical clustering can be accelerated through parallel computation while preserving the computational structure of different clustering metrics. Its main themes include minimum spanning trees, Prim’s algorithm, single-link clustering, nearest-neighbour methods, PRAM computation, parallel data structures and asymptotic complexity analysis. Prims_algorithm_for_hierarchica… Important: because this file is a journal research paper rather than a university assessment brief, fields such as module name, academic level, assignment type and formal word count do not genuinely apply.
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