| CODE | ICS5111 | ||||||||||||
| TITLE | Mining Large-Scale Data | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | This study-unit will present two important and complimentary aspects of how to deal with large-scale data, namely data mining and visualisation. In the first part of the study-unit the students will be exposed to various techniques that support three important aspects related to large data (structured or unstructured): (i) acquisition of information (ii) insight (identifying patterns and trends upon which to base actions), and (iii) prediction (modelling future activities or outcomes based on input data). The topics that will be tackled include: - Text classification and analytics; - Document clustering and summarisation; - Data cleanup techniques; - Frequent-itemset mining; - Feature selection; - Dimensionality reduction; - Algorithms for analysing and predicting trends from different data sources. In the second part of the study-unit the focus will be on graph mining. Students will become familiar with the graph data structure, the challenges of processing large amounts of data as graphs, state-of- the-art methods and algorithms or analysing graphs, and applications of graph mining in various domains. The topics that will be covered include: - Graph mining algorithms; - Network analysis; scale free networks, analysis metrics; - Community detection algorithms; - Graph databases; - Visualisation of graphs. Study-unit Aims: Through this study-unit, students will be given the opportunity to: - learn the relevant techniques in mining large data, the reasoning behind them and when to use them; - learn how to use a range of modelling and data analytical techniques; - evaluate the advantages and limitations of different technologies related to mining large data; - learn and work both independently and within groups; - develop balance between theoretical and practical skills. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - gain an in depth understanding of tools and techniques employed in mining information from unstructured sources; - develop methods for acquiring massive amounts of data in a form amenable to mining techniques; - analyse and evaluate the structure and properties of data to be selected, and devise appropriate methods for data exploration. 2. Skills: By the end of the study-unit the student will be able to: - employ surface based techniques for information extraction from unstructured text; - lean how to tackle specific data-intensive problems: association rules, rules, clustering; - select, use, and deploy specialised tools for mining large data. Main Text/s and any supplementary readings: - Leskovex, J., Leskovec, J., Rajaraman, A., Ullman, J. (2014) Mining of Massive Datasets, Cambridge University Press. - Han, J., Kamber, M., Pei, J. (2011) Data Mining: Concepts and Techniques, 3rd Edition, The Morgan Kaufmann Series in Data Management Systems), ISBN-13: 978-0123814791. |
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| STUDY-UNIT TYPE | Lecture | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Charlie Abela (Co-ord.) Nicholas Mamo |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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