What is knowledge discovery from data?

Overview

In today’s time, data is abundant. We have many forms of data. It is important to understand how to search for the right data and what actions should be performed on it.

Data Mining is the process of discovery of interesting patterns and knowledge from huge data. The data sources can include Databases, Data-warehouses, Web browsers, and other information repositories.

Note: Data mining is referred to as a synonym of KDD (Knowledge Discovery from Data).

KDD (Knowledge Discovery from Data) process

The KDD has the following iterative steps:

1. Data cleaning: It involves removing inconsistent data and noise.
2. Data integration: It involves combining data from various data sources and forming a single data source by integrating it all together.
3. Data selection: The data relevant to performing a particular task is selected as a part of it.
4. Data transformation: Data is transformed into appropriate form and transformation operations like summary or aggregation.
5. Data mining: It is an essential process where intelligent methods are applied to extract data patterns.
6. Pattern evaluation: The identified patterns are evaluated, and interesting patterns are represented as knowledge.
7. Knowledge representation: This is the final step of the KDD process in which visualizations are created using various graphical representation methods.

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