This document discusses concepts related to data mining input, including concepts, instances, and attributes. It also covers different types of learning in data mining like classification, numeric prediction, clustering, and association rules. Key steps to prepare data for mining are discussed, such as data assembly, integration, cleaning, and preparation. Formats for data like ARFF files and handling sparse data are also covered.

1. Data Mining Input: Concepts, Instances, and Attributes

2. Input takes the following forms:Concept: The thing that is to be learned is called the concept. Concept should be :

3. Intelligible in that it can be understood

4. Operational in that it can be applied to actual examples

5. Instances: The data present consists of various instances of the class. E.g. the table below consists of 2 instances

6. Attributes: Each instance of the class has various attributes. E.g. the table bellow consists of two attributes {Name, Age}Types of learning in data miningClassification learning:

7. Learning scheme is presented with a set of classified examples from which it is expected to learn a way of classifying unseen examples

8. Also called supervised learning

9. E.g. Classification rules for the weather forecasting problem If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes

10. Numeric prediction

11. Same as classification learning but the outcome to be predicted is not a discreet class but a numeric quantity

12. Clustering

13. Groups of examples that belong together are sought and clubbed together in a cluster

14. E.g. based on the data with a bank the following relation between debt and income was seen:Association rules

15. Any association among features is sought, not just ones that predict a particular class value

16. It predicts any attribute, not just the class

17. It can predict more than one attribute value at a time

18. E.g. from the following super market data it can be concluded: If milk and bread is bought, customers also buy butterFew important terms…Concept description: Output produced by a learning scheme