Teaching Responsibility
LJMU Schools involved in Delivery:
LJMU Partner Taught
Learning Methods
Lecture
Module Offerings
5557NCCG-JAN-PAR
5557NCCG-SEP-PAR
5557NCCG-SEP_NS-PAR
Aims
This module will introduce the theoretical foundation of data mining and knowledge acquisition together with practical experience of a range of related processes and techniques.
Learning Outcomes
1.
Discuss the historical and theoretical foundation of data mining, its scope, techniques, and processes
2.
Investigate a range of data mining techniques to discover patterns and relationships in large data sets
3.
Illustrate how a data mining algorithm performs text mining to identify relationships within text.
4.
Evaluate a range of graph data mining techniques that recognise patterns and relationships in graph-based technologies.
Module Content
Outline Syllabus:Data mining terminologies.
Scope of data mining: Classification, regression and clustering. Data mining algorithms: Classification algorithms, regression algorithms and clustering algorithms
Text mining. Overview to natural language processing. Document preparation and similarities. Clustering methods. Topic Modelling. Presentation methods of text
Patterns and relationships in data. Unstructured data and graph-based technologies. Networks and network analysis. Graph algorithms: graph pattern mining, graph classification, graph clustering, and so forth. Content mining, structure mining and usage mining. Graph data mining tools
Knowledge acquisition from data. Construction of knowledge-based systems.