Data Mining

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Data Mining | Universidad Pablo Olavide

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns. It is an interdisciplinary subfield of computer science.

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the knowledge discovery in databases process.

Data mining has become quite popular during the last decade, given the powerful techniques capable of exploiting large amounts of data.

Course Contents

  1. Introduction
  2. Knowledge Discovery in Databases (KDD)
  3. Data preprocessing
  4. Data transformation
  5. Data mining
    • Supervised learning
      • Classification
      • Regression
    • Unsupervised learning
      • Clustering
      • Association rules
    • Visualization
    • Evaluation