Data-Mining-1.pptxWERWEFWFWEFWEFWEWEFFEW

wqdasjdjspaj

1. Data Mining

2. Data Mining the process of discovering patterns and knowledge from large datasets. It’s not just about collecting data but making sense of it.

3. Cross-Industry Standard Process for Data Mining (CRISP-DM)

4. Data Mining Real-World Application • Market Analysis and Management • Corporate Analysis & Risk Management • Fraud Detection

5. Market Analysis and Management • Customer Profiling • Identifying Customer Requirements • Cross Market Analysis • Target Marketing • Determining Customer purchasing pattern • Providing Summary Information

6. Corporate Analysis and Risk Management • Finance Planning and Asset Evaluation • Resource Planning • Competitor

7. Fraud Detection Detect frauds, and analyzes patterns that deviate from expected norms

8. Data Mining Tasks Descriptive Classification and Prediction

9. Data Mining Tasks Descriptive – general properties of data. 1.1 Class/Concept Description 1.2 Mining of Frequent Patterns 1.3 Mining of Associations 1.4 Mining of Correlations 1.5 Mining of Clusters 2. Classification and Prediction – Classification is the process of finding a model that describes data classes or concepts, then uses it to predict the class of objects whose class label is unknown.

10. 1.1 Class/Concept Description – refers to the data to be associated with a class or concept. 1.1.1 Data Characterization – Summarizing data of the class under study, called the Target Class. 1.1.2 Data Discrimination – mapping or classification of a class with some predefined group or class.

11. 1.2 Mining of Frequent Patterns – patterns that occur frequently in transactional data. 1.1.1 Frequent Item Set – a set of items that frequently appear together. 1.1.2 Frequent Subsequence - a sequence of patterns that occur frequently. 1.1.3 Frequent Sub Structure – different structural forms, which may be co

12. 1.3 Mining of Association – used in retail sales to identify patterns that are frequently purchased together. 1.4 Mining of Correlations – additional analysis performed to uncover interesting statistical correlations between two items. 1.5 Mining of clusters - refer to a group of similar kind of objects.

13. I. Identification 1.Define data mining. 2.Name and explain the first step of CRISP-DM. 3.Give one real-world application of data mining. II. Enumeration List the six steps of CRISP-DM in order and briefly describe each.