Data Mining - Concepts and Techniques
Details
| ID | 2858813 |
| Duration | 2.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | Basics in Statistics |
| Target group | Information workers, IT professionals |
Overview
Data mining (the analysis step of the \"Knowledge Discovery in Databases\" process, or KDD) is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. 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.
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Content
Introduction to Data Mining
Overview: Why Data Mining? What Is Data Mining? What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? Which Technologies Are Used? - Data Preparation: Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Measuring Data Similarity and Dissimilarity - Data Preprocessing: Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization - Data Warehousing and Online Analytical Processing (OLAP)Data Mining for Frequent Patterns
Frequent Itemset Mining Methods - The Apriori Algorithm - Market Basket Analysis - Pattern Evaluation MethodClassification using Decision Trees
Decision Tree Induction - Attribute Selection Measures - Tree Pruning - Scalability and Decision Tree Induction - Rule-Based ClassificationClassification using Probabilistic Approaches
Bayes Classification Methods - Bayes´ Theorem –Naïve Bayes Algorithm – Bayesian Networks - Model Evaluation and Selection - Techniques to Improve Classification AccuracyClassification: Advanced Methods
Classification by Backpropagation and Artificial Neural Networks - Support Vector Machines - Lazy LearnersCluster Analysis
Overview of Basic Clustering Methods - Measuring Data Similarity and Dissimilarity: Data Matrix versus Dissimilarity Matrix, Proximity Measures for Nominal, Ordinal, and Binary Attributes, Dissimilarity of Numeric Data - Partitioning Methods (k-Means and k-Medoids) - Hierarchical Methods: Agglomerative versus Divisive Hierarchical ClusteringInstructor
Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.Publications
- Grundlagen empirische Sozialforschung (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9