Module Details |
| The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
| Title | Data Mining and Visualisation | ||
| Code | COMP337 | ||
| Coordinator |
Dr V Zamaraev Computer Science Viktor.Zamaraev@liverpool.ac.uk |
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| Year | CATS Level | Semester | CATS Value |
| Session 2021-22 | Level 6 FHEQ | Second Semester | 15 |
Aims |
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To provide an in-depth systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining. |
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Learning Outcomes |
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(LO1) A critical awareness of current problems and research issues in Data Mining |
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(LO2) A comprehensive understanding of current advanced scholarship and research in data mining and how this may contribute to the effective design and pmplementation of data mining applications. |
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(LO3) The ability of consistently apply knowledge concerning current data mining research issues in an original manner and produce work which is at the forefront of current developments in the sub-discipline of data mining. |
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(LO4) A conceptual understanding sufficient to evaluate critically current research and advanced scholarship in data mining. |
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(S1) Critical thinking and problem solving - Problem identification |
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(S2) Critical thinking and problem solving - Ciritcal analysis |
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Syllabus |
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Introduction to Data Mining, Text Mining, Data Warehousing, scope and challenges. Classification, problem definition, basic approaches (rules, trees). Advanced solutions to the challenges of classification and regression, evaluation possibilities for classification algorithms. Input preprocessing and hybrid solutions to data mining challenges. Association Rule Mining (ARM), problem definition, current challenges and solutions. Visualisation methods and their application to data mining will be studied using several freely available visualisation tools. Web mining and information retrieval systems. Learning ranking functions. Sequential/temporal data mining alogirithms. |
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Teaching and Learning Strategies |
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Teaching Method 1 - Lecture Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions. (b) Fully online delivery and assessment (c) Standard on-campus delivery |
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Teaching Schedule |
| Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
| Study Hours |
40 |
40 | |||||
| Timetable (if known) | |||||||
| Private Study | 110 | ||||||
| TOTAL HOURS | 150 | ||||||
Assessment |
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| EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
| 337 Final Exam Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :Semester 2 | 70 | |||||
| CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
| (337.1) Programming assignment 1 | 15 | |||||
| (337.2) Programming assignment 2 | 15 | |||||
Recommended Texts |
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| Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. | |