Data Mining

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Techniques for modelling and extracting features from non-textual data
• Theoretical concepts and the motivations behind different data-mining approaches
• Key concepts, tools and approaches for data mining on complex unstructured data sets (including multimedia mining, Twitter analysis, etc)
• Natural language processing techniques for extracting features from text
• State-of-the-art data-mining techniques including topic modelling approaches such as LDA, clustering techniques and applications of matrix factorisations
• The theory behind modern data indexing systems

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Solve real-word data-mining, data-indexing and information extraction tasks

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Conceptually understand the role of data-mining, together with the mathematical techniques this requires

Key concepts:

• The importance of data-mining
• Real-world applications of data-mining (cyber-security, financial forecasting, trend prediction, etc)
• What is unstructured data
• - Modalities of data
• Underlying techniques
• - Inverted indexes
• - Matrix factorisation
• - Dimensionality reduction

Modelling data:

• Understanding Text
• - Bags of Words
• - TF-IDF
• Dealing with non-textual data
• - Feature extraction techniques
• - Bags of features
• - Encoding and embedding

Modern data indexing at scale

• Information retrieval models
• Ranking models

Unimodal data mining:

• Topic modelling (techniques such as LSA, pLSA, LDA, NNMF)
• Clustering (Hierarchical agglomerative, Spectral)
• Multi-dimensional scaling
• Mining graphs and networks (hubs and authorities [PageRank/HITS], spectral methods, etc.)
• Finding outliers

Multimodal data mining:

• Finding independent features (e.g ICA, NNMF)
• Finding correlations and making predictions (CL-LSI, classifiers, etc.)
• Collaborative filtering and recommender systems

Resources & Reading list

Textbooks

Toby Segaran (2007). Programming Collective Intelligence: Building Smart Web 2.0 Applications. O'Reilly.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External