LibGuides: TIM-8131 v2: Module 2

Lesson 3 Required Resources

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Chapter 6: Mining frequent patterns (Sections 6.1–6.4, 6.6). In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. Access via O'Reilly Learning Platform
Read Chapter 6 (Sections 6.1–6.4, 6.6) to explore key methods for mining frequent patterns in large datasets. These sections introduce concepts like the Apriori algorithm and FP-Growth, which are foundational to association rule learning and pattern discovery.

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapter 2: Overview of supervised learning (Sections 2.5–2.7). In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/
Read Sections 2.5–2.7 to review key correlation concepts, including the relationship between predictors and response variables. These sections lay the groundwork for understanding how correlation influences model performance and interpretation.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapter 19.6: Structured probabilistic models. In Deep learning. MIT Press. https://www.deeplearningbook.org
Optional: Read Chapter 19.6 for an advanced introduction to structured probabilistic models. This section explores how deep learning frameworks can incorporate complex dependencies between variables using models like Markov random fields and conditional random fields.

Introduction to Data Mining
Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Chapter 5: Association analysis (Sections 5.1–5.4). In Introduction to data mining (2nd ed.). Pearson.

Read Chapter 5 (Sections 5.1–5.4) to explore the fundamentals of association analysis. This includes understanding frequent itemsets, association rules, and key algorithms like Apriori and FP-Growth for mining patterns in transactional data.

Data Mining: Concepts and Techniques
Han, J., Kamber, M., & Pei, J. (2012). Chapter 10: Cluster analysis (Sections 10.1–10.4). In Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://learning.oreilly.com/library/view/data-mining-concepts/9780123814791/
Read Chapter 10 (Sections 10.1–10.4) to explore the fundamentals of cluster analysis, including types of clustering, partitioning methods, and hierarchical approaches used in data mining.

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Chapter 14: Unsupervised learning (Sections 14.1–14.3). In The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://hastie.su.domains/ElemStatLearn/
Read Chapter 14 (Sections 14.1–14.3) to understand key unsupervised learning methods including clustering and principal components analysis. These techniques help uncover patterns in data without labeled responses.

Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Chapter 15: Autoencoders (Sections 15.1–15.4). In Deep learning. MIT Press. https://www.deeplearningbook.org
Read Chapter 15 (Sections 15.1–15.4) to learn about autoencoders and their role in clustering within feature space. These models are central to unsupervised representation learning and dimensionality reduction.

Introduction to Data Mining
Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Chapter 8: Cluster analysis (Sections 8.1–8.4). In Introduction to data mining (2nd ed.). Pearson.

Read Chapter 8 (Sections 8.1–8.4) to explore advanced topics in cluster analysis. This includes discussions on the characteristics of data and clustering algorithms, prototype-based clustering methods like fuzzy clustering and mixture models, density-based clustering techniques, and graph-based clustering approaches.