Data mining

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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: 183
Source areas: 10
Connected nodes: 193
Extracted relationships: 161
Related term clusters: 68
Bridge connections: 193

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 · 49 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.

Start with your topic. Discover where to go next.

Explore different angles and find fresh ideas to shape your next piece of content.

Data mining

14Data set · Machine learning · Statistics

13Misnomer · Pattern · Data scraping

7Cluster analysis · Anomaly detection · Dependency (computer science)

10Data · Bayes' theorem · Regression analysis

6Privacy · Ethics · National security

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

Situation in the United States

For the semantics nerds

You can skip this section if you’re here for content ideas and keyword inspiration.

Advanced semantic analysis

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, Angoss KnowledgeSTUDIO, Carrot2, Chemicalize, Data, DATADVANCE, ELKI, GATE, Genetic Programming, GNU Project, Google, Google Cloud Platform, Hewlett-Packard, IBM, Intelligent OptimizatioN, Java, KNIME, Learning, LION Another extracted example is Data mining → AAHC, According, Accountability Act, America, Biotech Business Week, Family Educational Rights, FERPA, Google Book, Google's, Health Insurance Portability, HIPAA, Israel, Privacy Act, South Korea, Taiwan, The HIPAA, United States, US, US Congress. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

has application · 61

Data mining → Amazon, Amazon SageMaker, Angoss KnowledgeSTUDIO, Carrot2, Chemicalize, Data, DATADVANCE, ELKI, GATE, Genetic Programming, GNU Project, Google, Google Cloud Platform, Hewlett-Packard, IBM, Intelligent OptimizatioN, Java, KNIME, Learning, LION

related to Situation in the United States · 19

Data mining → AAHC, According, Accountability Act, America, Biotech Business Week, Family Educational Rights, FERPA, Google Book, Google's, Health Insurance Portability, HIPAA, Israel, Privacy Act, South Korea, Taiwan, The HIPAA, United States, US, US Congress

related to Etymology · 11

Data mining → AI, Currently, Database Mining Workstation, Economic Studies, Gregory Piatetsky-Shapiro, HNC, KDD-1989, Lovell, Michael Lovell, Review, San Diego

related to Research · 11

Data mining → ACM, ACM SIG, Association, Computer, Computing Machinery's, Knowledge Discovery, SIG, SIGKDD, SIGKDD Explorations, Since, Special Interest Group

related to Standards · 10

Data mining → CRISP-DM, Data Mining Group, Development, DMG, European Cross-Industry Standard Process, Java Data Mining, JDM, PMML, Predictive Model Markup Language, XML-based

related to Data mining · 9

Data mining → Anomaly, Association, Classification, Clustering, Data, Regression, Searches, Summarization, Using

related to Situation in Europe · 7

Data mining → Edward Snowden's, Europe, European, National Security Agency, Safe Harbor Principles, United Kingdom, United States

is a · 4

Data mining → analysis step of the, interdisciplinary subfield of computer science and statistics with an overall goal of extracting information, process of applying these methods with the intention of uncovering hidden patterns. in large data sets, process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning

related to Results validation · 3

Data mining → Data, ROC, Several

related to background · 2

Data mining → Bayes, Data

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 161 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.

Subject	Predicate	Object	Confidence	Src
Data mining	is a	process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning	0.90	text
Data mining	is a	interdisciplinary subfield of computer science and statistics with an overall goal of extracting information	0.90	text
Data mining	is a	analysis step of the	0.90	text
Data mining	is a	process of applying these methods with the intention of uncovering hidden patterns. in large data sets	0.90	text
groups of data records	instance of	interesting patterns	0.80	text
spatial indices	instance of	This usually involves using database techniques	0.80	text
the ICDE Conference	instance of	ACM SIGKDD Conference on Knowledge Discovery and Data MiningData mining topics are also present in many data management/database conferences	0.80	text
SIGMOD Conference	instance of	ACM SIGKDD Conference on Knowledge Discovery and Data MiningData mining topics are also present in many data management/database conferences	0.80	text
International Conference on Very Large Data Bases	instance of	ACM SIGKDD Conference on Knowledge Discovery and Data MiningData mining topics are also present in many data management/database conferences	0.80	text
the Health Insurance Portability	instance of	privacy concerns have been addressed by the US Congress via the passage of regulatory controls	0.80	text
Accountability Act	instance of	privacy concerns have been addressed by the US Congress via the passage of regulatory controls	0.80	text
HIPAA	instance of	This underscores the necessity for data anonymity in data aggregation and mining practices.U.S. information privacy legislation	0.80	text

Related concept clusters Related term clusters

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
patterns
- Set
- Large
- Statistical
- Algorithms
- Knowledge
- Analysis
- May
- Sets
- Process
- Used
- Analytics
- Statistics

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 138 ⟂ 56

Data mining — Overview · splits 144 ⟂ 50

Data mining — Privacy concerns and ethics · splits 170 ⟂ 24

Data mining — Process · splits 174 ⟂ 20

Data mining — Background · splits 183 ⟂ 11

Data mining — Etymology · splits 186 ⟂ 8

Data mining — Research · splits 186 ⟂ 8

Data mining — Situation in the United States · splits 186 ⟂ 8

Data mining — Standards · splits 189 ⟂ 5

Data mining — Notable uses · splits 191 ⟂ 3

Map overview Semantic statistics

Data mining

Nodes194

Edges193

Triples161

Avg. degree1.99

Density0.010309

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

Data mining: Standards, Applications, Research & Science

Data mining topic overview

What this topic covers Research coverage

Key facts & relationships

Start with your topic. Discover where to go next.

Questions to explore

Explore all related topics Closing gaps

Overview

Etymology

Background

Process

Research

Standards

Notable uses

Privacy concerns and ethics

Situation in the United States

Software

For the semantics nerds

Advanced semantic analysis

How Data mining connects Entity context

Data mining

Top relations

Important terminology

Important terminology

Data mining relationships Subject–Predicate–Object triples

Related concept clusters Related term clusters

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Data mining

Source & methodology