Knowledge Discovery in Databases

What Does Knowledge Discovery in Databases Mean?

Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior knowledge on data sets and interpreting accurate solutions from the observed results.

Major KDD application areas include marketing, fraud detection, telecommunication and manufacturing.

Techopedia Explains Knowledge Discovery in Databases

Traditionally, data mining and knowledge discovery was performed manually. As time passed, the amount of data in many systems grew to larger than terabyte size, and could no longer be maintained manually. Moreover, for the successful existence of any business, discovering underlying patterns in data is considered essential. As a result, several software tools were developed to discover hidden data and make assumptions, which formed a part of artificial intelligence.

The KDD process has reached its peak in the last 10 years. It now houses many different approaches to discovery, which includes inductive learning, Bayesian statistics, semantic query optimization, knowledge acquisition for expert systems and information theory. The ultimate goal is to extract high-level knowledge from low-level data.

KDD includes multidisciplinary activities. This encompasses data storage and access, scaling algorithms to massive data sets and interpreting results. The data cleansing and data access process included in data warehousing facilitate the KDD process. Artificial intelligence also supports KDD by discovering empirical laws from experimentation and observations. The patterns recognized in the data must be valid on new data, and possess some degree of certainty. These patterns are considered new knowledge. Steps involved in the entire KDD process are:

1. Identify the goal of the KDD process from the customer’s perspective.
2. Understand application domains involved and the knowledge that’s required
3. Select a target data set or subset of data samples on which discovery is be performed.
4. Cleanse and preprocess data by deciding strategies to handle missing fields and alter the data as per the requirements.
5. Simplify the data sets by removing unwanted variables. Then, analyze useful features that can be used to represent the data, depending on the goal or task.
6. Match KDD goals with data mining methods to suggest hidden patterns.
7. Choose data mining algorithms to discover hidden patterns. This process includes deciding which models and parameters might be appropriate for the overall KDD process.
8. Search for patterns of interest in a particular representational form, which include classification rules or trees, regression and clustering.
9. Interpret essential knowledge from the mined patterns.
10. Use the knowledge and incorporate it into another system for further action.
11. Document it and make reports for interested parties.