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Data mining: Standards, Applications, Research & Science

Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and…

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Data mining topic overview

The analysis highlights Standards, Applications, Research and Science as prominent areas in the source structure around Data mining.

Related topics
184
Source areas
10
Connected nodes
194
Extracted relationships
230
Concept neighborhoods
68
Bridge connections
194

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Software · 55 topics
Overview · 50 topics
Privacy concerns and ethics · 23 topics
Process · 19 topics
Background · 10 topics
Etymology · 7 topics
Research · 7 topics
Situation in the United States · 7 topics
Standards · 4 topics
Notable uses · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Etymology

Background

Process

Research

Standards

Notable uses

Privacy concerns and ethics

Situation in the United States

Software

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Data mining connects Entity context

The extracted context around Data mining shows recurring relationship patterns in the source. For example, Data mining → Amazon, Amazon SageMaker, An, Angoss KnowledgeSTUDIO, Carrot2, Chemicalize, Data, DATADVANCE, ELKI, GATE, Genetic Programming, GNU Project, Google, Google Cloud Platform, Hewlett-Packard, IBM, Intelligent OptimizatioN, It, Java, KNIME Another extracted example is Data mining → AAHC, According, Accountability Act, America, As, Biotech Business Week, Family Educational Rights, FERPA, For, Google Book, Google's, Health Insurance Portability, HIPAA, In, Israel, More, Privacy Act, South Korea, Taiwan, The. Use these groups to spot repeated connection types before inspecting the individual relationships.

Data mining

Top relations

has application · 64
Data mining → Amazon, Amazon SageMaker, An, Angoss KnowledgeSTUDIO, Carrot2, Chemicalize, Data, DATADVANCE, ELKI, GATE, Genetic Programming, GNU Project, Google, Google Cloud Platform, Hewlett-Packard, IBM, Intelligent OptimizatioN, It, Java, KNIME
related to Situation in the United States · 25
Data mining → AAHC, According, Accountability Act, America, As, Biotech Business Week, Family Educational Rights, FERPA, For, Google Book, Google's, Health Insurance Portability, HIPAA, In, Israel, More, Privacy Act, South Korea, Taiwan, The
see also · 25
Data mining → Agent, Analytics, Approach, Categorization, Centralized, Competitive, Computational, Computer, Data, Decision, Discovery, Extremely, Grouping, Information, Method, Paradigm, Pharmaceutical, Probabilistic, Process, Research
related to Etymology · 16
Data mining → AI, Currently, Database Mining Workstation, Economic Studies, For, Gregory Piatetsky-Shapiro, HNC, However, In, KDD-1989, Lovell, Michael Lovell, Other, Review, San Diego, The
related to Standards · 14
Data mining → As, CRISP-DM, Data Mining Group, Development, DMG, European Cross-Industry Standard Process, For, However, Java Data Mining, JDM, PMML, Predictive Model Markup Language, There, XML-based
related to Data mining · 12
Data mining → Anomaly, Association, Classification, Clustering, Data, For, Regression, Searches, Summarization, The, This, Using
related to Research · 12
Data mining → ACM, ACM SIG, Association, Computer, Computing Machinery's, Knowledge Discovery, SIG, SIGKDD, SIGKDD Explorations, Since, Special Interest Group, The
related to Situation in Europe · 11
Data mining → As, Edward Snowden's, Europe, European, However, In, National Security Agency, Safe Harbor Principles, These, United Kingdom, United States
related to Results validation · 10
Data mining → Data, For, It, Not, Once, ROC, Several, The, This, To
related to background · 6
Data mining → As, Bayes, Data, Early, It, The

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

data mining patterns learning analysis machine information used set methods software process privacy knowledge also discovery use database statistical language

Data mining relationships Subject–Predicate–Object triples

TTTA extracted 230 structured relationships around Data mining. Examples in this analysis include Data mining → is a → process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning and Data mining → is a → interdisciplinary subfield of computer science and statistics with an overall goal of extracting information. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Data miningis aprocess of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning0.90text
Data miningis ainterdisciplinary subfield of computer science and statistics with an overall goal of extracting information0.90text
Data miningis aanalysis step of the0.90text
Data miningis aprocess of applying these methods with the intention of uncovering hidden patterns. in large data sets0.90text
groups of data recordsinstance ofinteresting patterns0.80text
spatial indicesinstance ofThis usually involves using database techniques0.80text
the ICDE Conferenceinstance ofACM SIGKDD Conference on Knowledge Discovery and Data MiningData mining topics are also present in many data management/database conferences0.80text
SIGMOD Conferenceinstance ofACM SIGKDD Conference on Knowledge Discovery and Data MiningData mining topics are also present in many data management/database conferences0.80text
International Conference on Very Large Data Basesinstance ofACM SIGKDD Conference on Knowledge Discovery and Data MiningData mining topics are also present in many data management/database conferences0.80text
the Health Insurance Portabilityinstance ofprivacy concerns have been addressed by the US Congress via the passage of regulatory controls0.80text
Accountability Actinstance ofprivacy concerns have been addressed by the US Congress via the passage of regulatory controls0.80text
HIPAAinstance ofThis underscores the necessity for data anonymity in data aggregation and mining practices.U.S. information privacy legislation0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Data mining bring nearby vocabulary together. In this analysis, examples include Mining, Used and Patterns. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Data mining
    • Mining
    • Used
    • Patterns
    • Analysis
    • Software
    • Set
    • Learning
    • Information
    • Use
    • Methods
    • Process
    • Machine
  • data mining
    • Mining
    • Software
    • Used
    • Patterns
    • Analysis
    • Process
    • Set
    • Learning
    • Information
    • Use
    • Knowledge
    • Methods
  • data sets
    • Mining
    • Statistics
    • Algorithms
    • Computer
    • Intelligence
    • Used
    • Large
    • Patterns
    • Analysis
    • Software
    • Information
    • Discovery
  • machine learning
    • Learning
    • Machine
    • Intelligence
    • Computer
    • Large
    • Algorithms
    • Analysis
    • Language
    • Statistics
    • Sets
    • Analytics
    • Information
  • computer science
    • Statistics
    • Machine
    • Information
    • Intelligence
    • Learning
    • Large
    • Business
    • Sets
    • Analysis
    • Algorithms
    • Use
    • Also
  • knowledge discovery in databases
    • Discovery
    • Knowledge
    • Step
    • Large
    • Algorithms
    • Process
    • Learning
    • Patterns
    • Intelligence
    • Statistics
    • Terms
    • Machine
  • data management
    • Mining
    • Used
    • Patterns
    • Analysis
    • Software
    • Set
    • Learning
    • Information
    • Methods
    • Process
    • Machine
    • Discovery
  • data pre-processing
    • Mining
    • Process
    • Used
    • Patterns
    • Analysis
    • Software
    • Set
    • Learning
    • Information
    • Methods
    • Machine
    • Discovery

Connections between topic areas Semantic bridges

For Data mining, one of the stronger structural bridges in this analysis connects Data mining with Software. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Data mining — Software · splits 139 ⟂ 56
Data mining — Overview · splits 144 ⟂ 51
Data mining — Privacy concerns and ethics · splits 171 ⟂ 24
Data mining — Process · splits 175 ⟂ 20
Data mining — Background · splits 184 ⟂ 11
Data mining — Etymology · splits 187 ⟂ 8
Data mining — Research · splits 187 ⟂ 8
Data mining — Situation in the United States · splits 187 ⟂ 8
Data mining — Standards · splits 190 ⟂ 5
Data mining — Notable uses · splits 192 ⟂ 3

Map overview Semantic statistics

Data mining

Nodes195
Edges194
Triples230
Avg. degree1.99
Density0.010256
Components1

Source & methodology

TTTA analyzes the structure around Data mining to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Standards, Applications, Research & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Data mining · EN edition · Analysis: TopicsToTalkAbout

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