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What is Knowledge Discovery in Databases (KDD)

Chapter 54
Handbook of Research on Public Information Technology
KDD is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data (Fayyad, 1996).
Published in Chapter:
Text Mining
Antonina Durfee (Appalachian State University, USA)
Copyright: © 2008 |Pages: 12
DOI: 10.4018/978-1-59904-857-4.ch054
Abstract
Massive quantities of information continue accumulating at about 1.5 billion gigabytes per year in numerous repositories held at news agencies, at libraries, on corporate intranets, on personal computers, and on the Web. A large portion of all available information exists in the form of text. Researchers, analysts, editors, venture capitalists, lawyers, help desk specialists, and even students are faced with text analysis challenges. Text mining tools aim at discovering knowledge from textual databases by isolating key bits of information from large amounts of text, identifying relationships among documents. Text mining technology is used for plagiarism and authorship attribution, text summarization and retrieval, and deception detection.
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Chapter 29
Fuzzy Sequential Patterns for Quantitative Data Mining
KDD is the automated process of turning raw data into useful information by which intelligent computer systems sift and sort through data to look for patterns or to predict trends. It is generally considered to be the nontrivial extraction of implicit, previously unknown, and potentially useful information from data.
Published in Chapter: Fuzzy Sequential Patterns for Quantitative Data Mining; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 144
Customer Relationship Management and Knowledge Discovery in Database
The process of data selection, sampling, pre-processing, cleaning, transformation, dimension reduction, analysis, visualization, and evaluation for the purpose of finding hidden knowledge from massive databases.
Published in Chapter: Customer Relationship Management and Knowledge Discovery in Database; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 33
Frequent Itemset Mining and Association Rules
A paradigm for the analysis of large datasets. The process is cyclic and iterative, with several steps including data preparation, analysis, and interpretation. KDD uses various methods from such diverse fields such as machine learning, artificial intelligence, pattern recognition, database management and design, statistics, expert systems, and data visualization.
Published in Chapter: Frequent Itemset Mining and Association Rules; From: Encyclopedia of Knowledge Management, Second Edition
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Chapter 1
Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers
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.
Published in Chapter: Machine Learning and Text Analysis in an Artificial Intelligent System for the Training of Air Traffic Controllers; From: Automated Systems in the Aviation and Aerospace Industries
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Chapter 67
Beyond Technology: An Integrative Process Model for Data Analytics
A term coined by Piatetsky-Shapiro in 1989 and later was adopted as a data analytics process for discovering useful knowledge from a collection of data proposed by Fayyad, Piatetsky-Shapiro, and Smyth in 1996.
Published in Chapter: Beyond Technology: An Integrative Process Model for Data Analytics; From: Encyclopedia of Data Science and Machine Learning
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Chapter 4
Use of Compensatory Fuzzy Logic for Knowledge Discovery Applied to the Warehouse Order Picking Problem for Real-Time Order Batching
The knowledge discovery can be defined as the non-trivial process of identifying valid, potentially useful and understandable patterns from a large amount of data. The fundamental objective of the KDD is to discover useful, valid, relevant and new knowledge through the use of various algorithms, that allows the improvement of processes.
Published in Chapter: Use of Compensatory Fuzzy Logic for Knowledge Discovery Applied to the Warehouse Order Picking Problem for Real-Time Order Batching; From: Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities
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Chapter 18
The Effectiveness of Big Data in Social Networks
The process of discovering useful information from a collection of raw data.
Published in Chapter: The Effectiveness of Big Data in Social Networks; From: Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence
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Chapter 28
Development and Design Methodologies in DWM
KDD is the process of extrapolating information from a database, from the identification of the initial business aims to the application of the decision rules (Giudici, 2003, p. 2).
Published in Chapter: Development and Design Methodologies in DWM; From: Encyclopedia of Decision Making and Decision Support Technologies
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Chapter 5
Machine Learning and Data Science Project Management From an Agile Perspective: Methods and Challenges
A method for discovering valuable knowledge from various sources of data.
Published in Chapter: Machine Learning and Data Science Project Management From an Agile Perspective: Methods and Challenges; From: Contemporary Challenges for Agile Project Management
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Chapter 131
Incorporating Fuzzy Logic in Data Mining Tasks
A nontrivial exploratory process of identifying valid, novel, useful, and understandable patterns from large and complex data repositories
Published in Chapter: Incorporating Fuzzy Logic in Data Mining Tasks; From: Encyclopedia of Artificial Intelligence
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