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CCleaner: History, Features & Malware infection
CCleaner (/ˈsiːˌkliːnər/; originally meaning "Crap Cleaner"), developed by Piriform Software, is a utility used to clean potentially unwanted files and invalid Windows Registry entries from a computer. It is one of the longest-established system cleaners, first launched in 2004. It was originally developed for Microsoft Windows only, but in 2012, a macOS…
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CCleaner topic overview
The analysis highlights History, Features and Malware infection as prominent areas in the source structure around CCleaner.
Related topics
69
Source areas
5
Connected nodes
74
Extracted relationships
106
Concept neighborhoods
23
Bridge connections
74
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.
Features · 38 topics
Malware infection · 12 topics
Critical reception · 10 topics
Overview · 6 topics
History · 3 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.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Android
26.12.2 / 9 July 2026; 46 days ago (2026-07-09)
Available in
58 languages
Developer
Piriform Software
iOS
1.15.1 / 27 April 2026; 3 months ago (2026-04-27)
License
Freemium for home use; commercial for use in organisations and institutions
macOS
2.09.187 / 19 October 2023; 2 years ago (2023-10-19)
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
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.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How CCleaner connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around CCleaner shows recurring relationship patterns in the source. For example, CCleaner → Adobe Acrobat, Adobe Flash Player, DLLs, Firefox, GIMP, Google Chrome, Google Toolbar, Internet Explorer, McAfee, MFT, Microsoft Edge, Microsoft Office, Nero, Netscape, Opera, Safari, Since, Sun Java, The, WinAce Another extracted example is CCleaner → After Piriform, Asus, Avast, CCleaner Cloud, Chunghwa, Dyn, Floxif, Forty, Fujitsu, Gauselmann, Intel, O2, October, On, Piriform, Samsung, September, Singtel, Sony, The. Use these groups to spot repeated connection types before inspecting the individual relationships.
CCleaner
Top relations
related to Features· 25
CCleaner → Adobe Acrobat, Adobe Flash Player, DLLs, Firefox, GIMP, Google Chrome, Google Toolbar, Internet Explorer, McAfee, MFT, Microsoft Edge, Microsoft Office, Nero, Netscape, Opera, Safari, Since, Sun Java, The, WinAce
related to Malware infection· 21
CCleaner → After Piriform, Asus, Avast, CCleaner Cloud, Chunghwa, Dyn, Floxif, Forty, Fujitsu, Gauselmann, Intel, O2, October, On, Piriform, Samsung, September, Singtel, Sony, The
related to Critical reception· 13
CCleaner → April, Chip, CNET, Editor's Choice Award, FileHippo, In, In January, It, PC Magazine, Piriform, Softpedia, TechRadar, TechRepublic
related to history· 13
CCleaner → Android, Apple App Store, It, January, June, Mac, Microsoft Windows, Network Edition, Nine, On, Piriform, The Mac, Windows-only
related to Bundled software· 8
CCleaner → Avast, Avast Antivirus, In December, In July, July, Microsoft Windows Defender, Piriform, TechSpot
related to Data collection· 8
CCleaner → Active Monitoring, After, Because, Data Factsheet, It, OS, Piriform, Smart Cleaning
CCleaner → 7.10.1464 / 29 July 2026; 26 days ago (2026-07-29), Windows7.10.1464 / 29 July 2026; 26 days ago (2026-07-29)macOS2.09.187 / 19 October 2023; 2 years ago (2023-10-19)Android26.12.2 / 9 July 2026; 46 days ago (2026-07-09)iOS1.15.1…
Android· 1
CCleaner → 26.12.2 / 9 July 2026; 46 days ago (2026-07-09)
Available in· 1
CCleaner → 58 languages
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 106 structured relationships around CCleaner. Examples in this analysis include CCleaner → Android → 26.12.2 / 9 July 2026; 46 days ago (2026-07-09) and CCleaner → Available in → 58 languages. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
CCleaner
Android
26.12.2 / 9 July 2026; 46 days ago (2026-07-09)
1.00
infobox
CCleaner
Available in
58 languages
1.00
infobox
CCleaner
Developer
Piriform Software
1.00
infobox
CCleaner
iOS
1.15.1 / 27 April 2026; 3 months ago (2026-04-27)
1.00
infobox
CCleaner
License
Freemium for home use; commercial for use in organisations and institutions
1.00
infobox
CCleaner
macOS
2.09.187 / 19 October 2023; 2 years ago (2023-10-19)
1.00
infobox
CCleaner
Operating system
Windows 10 and later, macOS Sierra and later, Android 10 and later, iOS 16 and later
1.00
infobox
CCleaner
Platform
IA-32 and x64
1.00
infobox
CCleaner
Release
23 September 2003; 22 years ago (2003-09-23)
1.00
infobox
CCleaner
Type
Utility software
1.00
infobox
CCleaner
Website
www.ccleaner.com
1.00
infobox
CCleaner
Windows
Windows7.10.1464 / 29 July 2026; 26 days ago (2026-07-29)macOS2.09.187 / 19 October 2023; 2 years ago (2023-10-19)Android26.12.2 / 9 July 2026; 46 days ago (2026-07-09)iOS1.15.1…
1.00
infobox
CCleaner
Windows
7.10.1464 / 29 July 2026; 26 days ago (2026-07-29)
1.00
infobox
CCleaner
Written in
C++
1.00
infobox
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around CCleaner bring nearby vocabulary together. In this analysis, examples include Piriform, Windows and Malware. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
CCleaner
Piriform
Windows
Malware
Software
Avast
Also
Installed
Media
Potentially
Programs
Unwanted
Program
ccleaner
Piriform
Windows
Malware
Software
Avast
Also
Installed
Media
Potentially
Programs
Unwanted
Program
windows registry
Microsoft
Used
Software
Potentially
Unwanted
Ccleaner
Application
System
Version
Cleaner
Developed
Entries
microsoft windows
Microsoft
Windows
Potentially
Unwanted
Software
Application
Ccleaner
System
Version
Cleaner
Developed
Entries
windows media player
Microsoft
Software
System
Utility
Potentially
Unwanted
Ccleaner
Application
Version
Cleaner
Developed
Entries
windows defender
Microsoft
Software
Potentially
Unwanted
Ccleaner
Application
System
Version
Cleaner
Developed
Entries
Files
piriform
Program
Data
Avast
Software
Used
Windows
Android
Collection
Potentially
Unwanted
Malware
Released
potentially unwanted application
Unwanted
Software
Application
Potentially
Utility
Microsoft
Program
Windows
Registry
Used
Cleaner
Entries
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For CCleaner, one of the stronger structural bridges in this analysis connects CCleaner with Features. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
CCleaner
Nodes75
Edges74
Triples106
Avg. degree1.97
Density0.026667
Components1
Source & methodology
TTTA analyzes the structure around CCleaner to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Features & Malware infection, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
