This course addresses principles, methods and practical recommendations for extracting interesting and meaningful patterns from structured and unstructured data (numeric and textual data), from a perspective at the interface between Computer Science and Statistics. The course covers fundamental topics and computational methods for the growing field of Data Analytics and Mining.

The course is organized in two modules:

(i) Module I is about data pre-processing, dimensionality reduction and data-driven clustering, to induce models from data and their interpretation aids.

(ii) Module II is about Relevant Information Extraction, symbolic and statistical analysis of texts, document descriptors, document classification and distribution of words and multi-words in Big Data context.

Knowledge:

• Understand the paradigms and challenges of Data Analytics and Text Mining
• Learn the fundamental methods and their applications in the extraction of patterns from data. Understand data features, the selection of models and interpretation of model’s results.
• Understand the advantages and disadvantages of the different methods.

Skills

• Implement and adapt Data Analytics and Text Mining algorithms;
• Model real data experimentally.
• Assessment and interpretation of experimental results.

Competences

• Ability to choose and evaluate the suitability of methods to case studies
• Abstraction and generalisation skills
• Critical analysis skills
• Search of scientific literature
• Autonomy and self-reliance in the application and furthering studies in Data Analytics and Text Mining.

Introduction

Data Analytics

What is data? Examples of data analytic tasks and various perspectives of them

Text Mining

Structured or unstructured data? Why mining texts?

What types of problems can be solved?

• Module I

Data Understanding

• 1D Summarization and Visualization of a Single Feature
• 2D Analysis: Correlation and Visualization of Two Quantitative Features
• Verification of structure in data
• Why normalization matters

Descriptive Modeling I

Principal Component Analysis(PCA): Model and Method

• Summarization versus Correlation
• Matrix spectrum and Singular Value Decomposition (SVD)
• PCA as SVD. Conventional PCA’s.

PCA: Applications

Descriptive Modeling II

• K‐means, Anomalous clusters, IntelligentK‐Means
• Spectral clustering
• Fuzzy clustering

Interpreting Descriptive Models

• Conventional Cluster Model Interpretation
• Assessing Cluster Tendency
• Least squares principle induced interpretation aids

Data Analytics Case Studies

• Module II‐Text Mining

Relevant Information Extraction

• Relevant Expressions: Multi‐words and single‐words
• Statistical vs symbolic extractors. Algorithms and metrics
• Language‐independence

Symbolic and Statistical Analysis of texts

• Tokenization, Stemming and Part‐Of‐Speech Tagging
• Word and Multi-word distribution in Big Data context. Zipf Law
• Metrics for word association and retrieval
• Document correlation
• Word Sense Disambiguation

Document Descriptors

• Language‐independent Mining of Explicit and Implicit Keywords from documents.
• Semantic Scope of Documents
• Document Summarization

Document Classification

• Relevant Expressions as features for document characterization. Feature selection and reduction.
• Document Similarity
• Supervised vs unsupervised Document Clustering.
• Prediction and evaluation

Text Mining Case Studies(some examples)

• Extraction of Named Entities
• Email filtering
• Language detection
• Efficient Extraction of Multiwords
• Polarity Detection

• Zaki, M., and Meira Jr, W., (2020), Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Cambridge University Press (2nd Edition)
• Larose, D. T. , Larose C. D. (2015), Data Mining and Predictive Analytics, Wiley (2nd Edition)
• Mirkin, B. (2019) Core Data Analysis: Summarization, Correlation, and Visualization, Springer
• Nascimento, S. (2005). Fuzzy Clustering via Proportional Membership Model, Frontiers of Artificial Intelligence and Applications, v 119, IOS Press
• Weiss, S.M., Indurkhya, N., Zhang, T., Damerau, F. (2005), Text Mining: Predictive Methods for Analyzing