Data Mining
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Data Mining: Discovering Patterns and Relationships in Data, Slides of Business Ethics
Data mining is an essential information analysis tool that involves the automated discovery of patterns and relationships in large datasets. This process, also known as knowledge discovery in databases (KDD), extracts implicit, previously unknown, and potentially useful information from data through various techniques such as clustering, data summarization, classification, dependency network analysis, and anomaly detection. Data mining applications include customer segmentation, trend analysis, financial statement analysis, loan application rating, vendor analysis, and problem employee identification. Technologies used in data mining include neural networks, rule induction, evolutionary programming, case-based reasoning, decision trees, generic algorithms, and non-linear regression methods.
What you will learn
- What are some applications of data mining in business and finance?
- What are some common techniques used in data mining?
- What is data mining and how does it differ from KDD?
Typology: Slides
2020/2021
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Data Mining
Defining Data Mining
◦ Data mining is an information analysis tool that involves the automated discovery of patterns and relationships in a data warehouse. ◦ Data mining also known as knowledge discovery databases (KDD), is the non trivial extraction of implicit, previously unknown and potentially useful information from the data. ◦ Data mining encompasses technical approaches such as clustering, data summarization, classification, finding dependency networks, analysing changes and detecting anomalies.
How does data
mining work
◦ Understanding the situation ◦ Developing suitable models ◦ Undertaking analysis based on suitable models. ◦ Initiating appropriate action ◦ Measuring the results ◦ Iterations
Technologies
used in Data
Mining
◦ Neural networks ◦ Rule induction ◦ Evolutionary programming ◦ Case based reasoning ◦ Decision trees ◦ Generic Algorithm ◦ Non-linear regression methods