1 VM Giriş

Introduction to Data Mining

• Instructor: Prof. Dr. Efendi Nasiboğlu

• References:

  • Vahaplar, A. (2014) Lecture notes.

  • Han, J., Kamber, M., Pei, J. (2011). Data Mining: Concepts and Techniques.

  • Larose, Daniel T. (2005). Discovering Knowledge in Data – An Introduction to Data Mining.

  • Tan, P., Steinbach, M., Kumar, V. (2006). Introduction to Data Mining.

  • Bramer, M. (2007). Principles of Data Mining.

  • Birant, D. (2012) Lecture Notes.

Contents Overview

• Databases and Data Structures:

  • Database, data warehouse, OLAP.

• Data Mining Processes:

  • Data mining process, CRISP-DM methodology.

  • Data preparation (veri hazırlama).

• Learning Types:

  • Unsupervised learning (denetimsiz öğrenme), including clustering.

    • Hierarchical clustering.

    • K-means, density-based clustering.

  • Supervised learning (denetimli öğrenme).

    • Classification methods.

    • k-nearest neighbor method.

    • Decision tree algorithms: CART, C4.5, CHAID, QUEST.

    • Neural networks (yapay sinir ağları).

• Other Concepts:

  • Association rules.

  • Model evaluation (model geçerliliği).

  • Project presentations (proje sunumları).

Motivation for Data Mining