# 3.1.11 data mining, ISO 22989 Published contents Contents1 Scope2 Normative references3 Terms and definitions4 Abbreviated terms5 AI conceptsClause 3, terms 3.1 Terms related to AI3.1.1 AI agent3.1.2 AI component3.1.3 artificial intelligence3.1.4 artificial intelligence system3.1.5 autonomy3.1.6 application specific integrated circuit3.1.7 automatic3.1.8 cognitive computing3.1.9 continuous learning3.1.10 connectionism3.1.11 data mining3.1.12 declarative knowledge3.1.13 expert system3.1.14 general AI3.1.15 genetic algorithm3.1.16 heteronomy3.1.17 inference3.1.18 internet of things3.1.19 IoT device3.1.20 IoT system3.1.21 knowledge3.1.22 life cycle3.1.23 model3.1.24 narrow AI3.1.25 performance3.1.26 planning3.1.27 prediction3.1.28 procedural knowledge3.1.29 robot3.1.30 robotics3.1.31 semantic computing3.1.32 soft computing3.1.33 symbolic AI3.1.34 subsymbolic AI3.1.35 task3.2 Terms related to data3.2.1 data annotation3.2.2 data quality checking3.2.3 data augmentation3.2.4 data sampling3.2.5 dataset3.2.6 exploratory data analysis3.2.7 ground truth3.2.8 imputation3.2.9 input data3.2.10 label3.2.11 personally identifiable information3.2.12 production data3.2.13 sample3.2.14 test data3.2.15 validation data3.3 Terms related to machine learning3.3.1 Bayesian network3.3.2 decision tree3.3.3 human-machine teaming3.3.4 hyperparameter3.3.5 machine learning3.3.6 machine learning algorithm3.3.7 machine learning model3.3.8 parameter3.3.9 reinforcement learning3.3.10 retraining3.3.11 semi-supervised machine learning3.3.12 supervised machine learning3.3.13 support vector machine3.3.14 trained model3.3.15 training3.3.16 training data3.3.17 unsupervised machine learning3.4 Terms related to neural networks3.4.1 activation function3.4.2 convolutional neural network3.4.3 convolution3.4.4 deep learning3.4.5 exploding gradient3.4.6 feed forward neural network3.4.7 long short-term memory3.4.8 neural network3.4.9 neuron3.4.10 recurrent neural network3.5 Terms related to trustworthiness3.5.1 accountable3.5.2 accountability3.5.3 availability3.5.4 bias3.5.5 control3.5.6 controllability3.5.7 explainability3.5.8 predictability3.5.9 reliability3.5.10 resilience3.5.11 risk3.5.12 robustness3.5.13 stakeholder3.5.14 transparency3.5.15 transparency3.5.16 trustworthiness3.5.17 verification3.5.18 validation3.6 Terms related to natural language processing3.6.1 automatic summarization3.6.2 dialogue management3.6.3 emotion recognition3.6.4 information retrieval3.6.5 machine translation3.6.6 named entity recognition3.6.7 natural language3.6.8 natural language generation3.6.9 natural language processing3.6.10 natural language processing3.6.11 natural language understanding3.6.12 optical character recognition3.6.13 part-of-speech tagging3.6.14 question answering3.6.15 relationship extraction3.6.16 sentiment analysis3.6.17 speech recognition3.6.18 speech synthesis3.7 Terms related to computer vision3.7.1 computer vision3.7.2 face recognition3.7.3 image3.7.4 image recognitionClause 5, concepts 5.1 General5.2 From strong and weak AI to general and narrow AI5.3 Agent5.4 Knowledge5.5 Cognition and cognitive computing5.6 Semantic computing5.7 Soft computing5.8 Genetic algorithms5.9 Symbolic and subsymbolic approaches for AI5.10 Data5.11 Machine learning concepts Home / Vocabulary Terms related to AI · 3.1.11 # data mining a computational method that looks across quantitative data from several angles to surface patterns, sort them and summarise likely links and effects - definition - Defined term - ISO/IEC 22989:2022/FDAmd 1 - Last updated: 2026-10-21 ## What this term settles The term looks ordinary until two teams use it differently. The entry removes that room by naming the thing, its inputs and its limits in one sentence. ## Who it binds The clause does not instruct anyone: it settles what data mining means so that an instruction written elsewhere can be read the same way by both sides. ## What to do in your practice Write the entry into the glossary the firm actually reads, not a shelf document. Check the two artefacts where the word does most work and note any sentence that stretches the meaning. ## Evidence an auditor or regulator asks for - A controlled vocabulary entry for data mining with the citation 3.1.11, an owner and a review date. An auditor can check that data mining is controlled rather than assumed. - An AI system inventory field that records the entry for each system, with its owner. The row names data mining. Shows data mining in use in the artefact the firm relies on, not only in a glossary. - A policy or procedure sentence that uses the entry in this sense. The record carries the data mining entry and its date. The policy borrows the word but not the boundary, so a supplier system and an in-house model are treated as the same kind of thing. The gap is tested against data mining. Ask your teamWhich artefact would a reviewer open to see data mining used in this sense? ## Common gaps this page catches - data mining is used in the AI vocabulary a policy reaches for first without a written definition, so two teams can mean different things. - The entry for data mining exists in a glossary but not in the system record, so the two disagree at the point of use. ## What to prepare before the amendment lands Add the entry to the register, give it an owner and a review date, and test it against one live artefact this week. The test is the evidence; the register row alone is not. ## How to test it here Take one live artefact rather than a template, find the sentence or field where data mining is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention. ## What this page does not claim The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states data mining at the claim level "definition" and stops where the public record stops. ## Common questions What does data mining mean in ISO 22989? a computational method that looks across quantitative data from several angles to surface patterns, sort them and summarise likely links and effects What evidence should we keep for 3.1.11? A controlled vocabulary entry for data mining with the citation 3.1.11, an owner and a review date. An AI system inventory field that records the entry for each system, with its owner. The row names data mining. Is data mining a requirement? No: data mining is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it. ## Related units - 3.1.10 connectionism - 3.1.12 declarative knowledge - 3.1.13 expert system Source. ISO/IEC 22989:2022, Clause 3.1.11, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard. Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text. Built from the public ISO record and the public clause 3 entries. No clause text is reproduced. How the record is built. Last updated: 2026-10-21 · Markdown version · Check your vocabulary Source: https://iso-22989-revision-checker.theartofservice.com/glossary/data-mining/