From Data to Knowledge: Data Mining

From Data to Knowledge: Data Mining

Tri Wijaya
Copyright: © 2013 |Pages: 13
DOI: 10.4018/978-1-4666-2455-9.ch059
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Abstract

This chapter will discuss a very useful technique to get (or to mine) a hidden information or knowledge which is lie in our data namely, data mining, which is a powerful and automatic (or semi-automatic) technique. Not only about the concept and theory, this chapter will also discuss about the application and implementation of data mining. Firstly, the authors will talk about data, information, and knowledge, whether they are different or not. After understand the term, they will discuss about what data mining is and what the importance of it. Second, they describe the process of gaining the hidden knowledge, how it is done, from the beginning until presenting the result. The authors will go through it step by step. In the next section, they will discuss about the several different tasks of data mining. In addition, to get a better understanding, the authors will compare data mining with other terminology which closely related so called data warehouse, and OLAP. For the last, but not the least, as stated before, this chapter will tell us about the real implementation of data mining in several different areas.
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Introduction

Data, Information and Knowledge

Before discussed any further about data mining, it is better for us to describe first what are data, information, and knowledge.

According to Palace (1996), Data are any facts, numbers, or text that can be processed by a computer. Recently, there are huge growing amounts of data in different formats and different databases. This includes:

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    Operational or transactional data: data that are obtained from sales, cost, inventory, payroll, and accounting.

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    Non-operational data: data are obtained from forecasting, industry, and macro-economic data.

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    Metadata: that is data about the data itself, such as logical database design or data dictionary definitions

From data we can get information, which is the patterns, associations, or relationships among all the data. For example, from the analysis of point of sale transaction we can obtain information about which products are sold and when.

Furthermore, information can be converted into knowledge about historical patterns and future trends. For instance, the monthly information from the point of sale transaction can be analyzed to understand the consumer buying behavior. As a consequence, the retailer could determine which promotional effort is worth, or which products are going to be advertised more than the others.

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