A Practical Guide to Data Mining for Business and Industry

2 Data mining definition

Data Mining Definition

1. 2.1 Types of Data Mining Questions
   1. 2.1.1 Population and Sample
   2. 2.1.2 Data Preparation
   3. 2.1.3 Supervised and Unsupervised Methods
   4. 2.1.4 Knowledge-Discovery Techniques
2. 2.2 Data Mining Process
3. 2.3 Business Task: Clarification of the Business Question behind the Problem
4. 2.4 Data: Provision and Processing of the Required Data
   1. 2.4.1 Fixing the Analysis Period
   2. 2.4.2 Basic Unit of Interest
   3. 2.4.3 Target Variables
   4. 2.4.4 Input Variables/Explanatory Variables
5. 2.5 Modelling: Analysis of the Data
6. 2.6 Evaluation and Validation during the Analysis Stage
7. 2.7 Application of Data Mining Results and Learning from the Experience

2.1 Types of Data Mining Questions

Data mining covers a wide range of activities. It seeks to provide the answer to questions such as these:

• What is contained in the data?
• What kinds of patterns can be discerned from the maze of data?
• How can all these data be used for future benefit?

2.1.1 Population and Sample

In data mining, datasets can be enormous – there may be millions of cases. Different types of industry, however, vary a lot as regards the number of cases emerging from the business processes. Web applications, for example, may collect data from millions of cookies, whereas other applications, like loyalty clubs or CRM programmes, may have more limited cases. Data protection laws and local market and industry customs vary, but in many countries, it is possible to purchase or to rent information at ...