1. Intelligent Systems Lecture 23 Introduction to Intelligent Data Analysis (IDA). Example of system for Data Analyzing based on neural networks

2. Synonyms of IDA Data mining Knowledge discovery, in particular, Knowledge Discovery in Databases (KDD) Knowledge acquisition

3. Data Mining: Definition Data Mining is a collection of technologies which employ modern analysis techniques and computer power to find structure in data. Data Mining is composed of several branches, each of which attempts to solve a different problem.

4. Place of KDD in Decision support system (in medicine)

5. Place of Data Mining in Knowledge Discovery (Jiavei Han, Micheline Kamber “Data Mining. Concepts and techniques”)

6. Data Mining: Ontology Predictive Modeling Clustering Deviation Detection Text Mining Link Analysis Etc....

7. Data Mining: Modeling Predictive Modeling attempts to predict values of future cases based on historical examples 90+% of all data mining is modeling Two major types: –estimation –classification Related to statistical regression

8. Data Mining: Clustering Clustering attempts to separate cases into ‘natural’ groupings. Strictly, there are no ‘right answers’ Tends to be compute intensive Very popular in marketing, as market segmentation

9. Data Mining: Deviation Detection Deviation detection identifies cases which are ‘unexpected’ All algorithms assume some model of ‘normality’- what is expected Related to outlier detection in conventional statistics

10. Data Mining: Text Mining Text mining includes several, different methods for discovering structure in documents. The most common text mining applications are search and retrieval tools.

11. Data Mining: Link Analysis Link analysis searches for indirect connections between items Primarily employed in fraud detection and law enforcement Link analysis tools typically include a data visualization component

12. Structure of software AnalDB Shell DLL … Modules with NNs Database User BDE GUI, generation of reports Control of NN Reading from Database, Options, Commands