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.
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:
Key steps in the Knowledge Discovery cycle:
Major KDD application areas include marketing, fraud detection, telecommunication and manufacturing.
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:
Key steps in the Knowledge Discovery cycle:
1.Data Cleaning: remove noise and incosistent data
2.Data Integration: combine multiple data sources
3.Data Selection: select the part of the data that are relevant for
the problem
4.Data Transformation: transform the data into a suitable format
(e.g., a single table, by summary or aggregation operations)
5.Data Mining: apply machine learning and machine discovery
techniques
6.Pattern Evaluation: evaluate whether the found patterns meet
the requirements (e.g., interestingness)
7.Knowledge Presentation: present the mined knowledge to the
user (e.g., visualization)
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