Knowledge Discovery in Databases: Definition, Examples


Knowledge Discovery in Databases (KDD) is a process that involves the use and analysis of large data sets to uncover hidden patterns, correlations, or other useful information. It includes several stages including data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation. Through KDD, organizations can make better decisions and predictions based on the insights drawn from their data.


The phonetics of the keyword “Knowledge Discovery in Databases” is:Näl-ij Dis-kə-və-rē in Dā-tə-bā-siz

Key Takeaways


  1. Data preprocessing: Knowledge discovery in databases involves extensive data preprocessing. This includes data cleaning to remove noise and inconsistent data, data integration to combine multiple data exchanges, data selection to focus on the analysis-relevant data, and data transformations to form appropriate format for mining.
  2. Mining of substantial patterns: In the process of Knowledge Discovery in Databases, one of the important stages includes mining for relevant data patterns. Tools and techniques used for this purpose are based on mathematical algorithms which assist in the evaluation of the probability of future events, prediction of outcomes, and establishment of relationships amongst data entities.
  3. Knowledge evaluation: One vital aspect of this process is the evaluation of knowledge. This is where discovered knowledge is visualized and evaluated before being used. The purpose is to remove or improve the parts of the data mining results that are less relevant or useful, in order to obtain reliable, actionable and comprehensive extracted knowledge.



Knowledge Discovery in Databases (KDD) is crucial in technology due to its role in deciphering large amounts of data and extracting pertinent, non-trivial information, often transforming it into a human-understandable structure. As data accumulates drastically in various domains such as business, health care, science, and social networks, the necessity of tools that can analyze and interpret this data becomes more prevalent. KDD provides a powerful solution, enabling the identification of patterns and correlations within data sets that could provide significant insights and drive important decision-making processes. Its importance extends to diverse fields, including artificial intelligence, machine learning, statistics, and database systems ultimately facilitating more informed and data-driven actions.


Knowledge Discovery in Databases (KDD) primarily serves the purpose of identifying valid, novel, potent and ultimately comprehensible patterns from large and complex sets of data. This technology term is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. The notion of KDD is about discovering insights and identifying patterns from large data sets — it’s a step-focused approach which involves pre-processing data, choosing data, cleaning data, implementing data changes, finding data patterns, and interpreting the results.KDD is commonly used for various purposes, most notably in making significant business decisions. For instance, it’s employed heavily within the fields of market research, medical research, fraud detection, and risk management. With the rise in the vastness of data being collected in multiple sectors, businesses and organizations use KDD to leverage their amassed data, extract valuable knowledge, and gain a competitive edge. By utilizing KDD, the ability to predict outcomes and trends is greatly increased, therefore enabling more pointed and informed decisions for the future.


1. Medical Research: In the healthcare industry, Knowledge Discovery in Databases (KDD) has played a significant role in understanding different diseases and their treatments. For example, in cancer research, KDD has been used to identify patterns and correlations from huge medical datasets to predict the likelihood of cancer, its types, stages, and effective treatments based on the patient’s medical history.2. Retail Industry: E-commerce and retail companies like Amazon and Walmart utilize KDD to analyze huge volumes of purchase data to discover buying patterns and predict future sales. This helps them in effective stock management, setting up marketing and promotional strategies, and improving customer service by providing personalized recommendations.3. Financial Fraud Detection: Banks and financial institutions use KDD to identify patterns of unusual transactions which could potentially be fraudulent. By analyzing huge volumes of transaction data, they can identify if there’s any unusual client behavior and potentially stop fraud before it happens. This helps in ensuring the safety of the client’s money and trust in the banking system.

Frequently Asked Questions(FAQ)

Q1: What is Knowledge Discovery in Databases (KDD)?A1: Knowledge Discovery in Databases (KDD) is the process of discovering useful knowledge from a collection of data. It involves the use of database systems and technology to explore and analyze large amounts of data to find meaningful patterns and rules.Q2: Why is Knowledge Discovery in Databases important?A2: KDD helps in making sense of large amounts of raw data and converting it into useful information. It plays a critical role in decision-making processes, offering insights, predicting future trends and behaviors, and making data-driven decisions.Q3: What is the difference between KDD and Data Mining?A3: Data mining is a step within the broader Knowledge Discovery in Databases (KDD) process. It involves the extraction of patterns from large data sets. KDD, on the other hand, involves several steps including data selection, data cleaning, data transformation, data mining, and interpretation/evaluation of the mined patterns.Q4: Which fields can benefit from Knowledge Discovery in Databases?A4: Numerous fields ranging from business, medicine, science, and engineering to art and humanities can benefit from KDD. Any field that collects and analyzes large amounts of data can potentially utilize KDD to find meaningful patterns and rules.Q5: What are some techniques used in Knowledge Discovery in Databases?A5: Some of the common techniques used in KDD include clustering, classification, regression, association rule learning, and anomaly detection. These techniques help in discovering patterns in the data.Q6: What are the challenges faced in Knowledge Discovery in Databases?A6: Some of the challenges include dealing with noisy and inconsistent data, handling the complexity of the data and algorithms, and ensuring privacy and data security. Additionally, interpreting the results correctly and effectively can also be challenging.Q7: How is machine learning related to Knowledge Discovery in Databases?A7: Machine learning is a technique often used in the data mining phase of KDD. Machine learning algorithms facilitate the automated discovery of patterns and the development of intelligent models based on those patterns.Q8: How does artificial intelligence tie into Knowledge Discovery in Databases?A8: Artificial intelligence (AI) collaborates with KDD by providing intelligent agents for pattern recognition, learning, and adaptation in the discovery algorithms. AI can automate and optimize the KDD process, enhancing the effectiveness and efficiency of knowledge discovery.

Related Tech Terms

  • Data Mining
  • Pattern Recognition
  • Data Preprocessing
  • Data Warehousing
  • Information Extraction

Sources for More Information

Table of Contents