Introduction to Data Mining

(This article is for study notes, courses from Bigdata university:http://bigdatauniversity.com.cn/courses/bigdatauniversity/pa0101/2016_06/ courseware/c4323451afcd4b05946917efc8fc86f5/be5f0606db034b559b014e87ab62e418/)

Why are we do data mining?

Market Context.

Analytics Drive decision-making.

Information Age:terabytes and petabytes of data available. How does we consume this data, translate it to information and make it usable?

What is data mining?

Process of discovering insights, patterns and relationships from large amounts of data.

What knowledge can extracted?

Descriptive:what has happened and so did it happen?

Predictive:what is likely to happen next

What can we learn?

• Association Rules: Rules that indicate relationships. For example, people who buy diapers also buy beer.

• Classification: Finding a model that describes the data and classifies it to a set of categories. For example, people who drink and drive is more likely to has higher insurance rates.

• Segmentation: Grouping objects by similarity. For example, prospective customers is broken up to clusters of suburban families with children, single college students , Urban empty nesters.

The key things:

Allow us to uncover inside information.

SPSSMODELER:CRISP-DM (cross-industry Process for Data Mining)

1. Iterative process
2. Business understanding
3. Data Understanding
4. Data Preparation
5. Modeling
6. Evaluation
7. Deployment

SOFTWARE:IBM SPSS Modeler

Introduction to Data Mining