- Home
-
Board
- Editorial Board
- Apply For Reviewer
-
Research Publishing Standards
- Guidelines
- Peer Review Policy
- Short Guide to New Editors
- Retraction Guidelines
- Research Institute
- Recommendations for the Board of Directors
- Editor Research Audit and Service Assessments
- Guidelines for Journals Publishers
- Code of Conduct for Journal Publishers
- Commandment in Social Media
- Suggestions
- Authorship Disputes
- Rights and Responsibilities
- Editors Home
- Visibility
- Information
- Apply for Editorial
- About
- Call for Paper
- Research Community
-
Journals
- Computer Science
-
Medical Research
- A: Neurology & Nervous System
- B: Pharma, Drug Discovery, Toxicology & Medicine
- C: Microbiology & Pathology
- D: Radiology, Diagnostic Imaging and Instrumentation
- E: Gynecology & Obstetrics
- F: Diseasescancer, ophthalmology & pediatric
- G: Veterinary Science & Veterinary Medicine
- H: Orthopedic & Musculoskeletal System
- I: Surgeries & Cardiovascular System
- J: Dentistry & Otolaryngology
- K: Interdisciplinary
- L:Nutrition and Food Science
- Engineering
- Science Frontier Research
- Management
-
Human-Social Science
- A: Arts & Humanities psychology, public administration, library sciences, sports, arts, media, music
- B: Geography, Environmental Science & Disaster Management
- C: Sociology & Culture
- D: History, Archaeology & Anthropology
- E: Economics
- F: Political Science
- G: Linguistics & Education
- H: Interdisciplinary
-
Fellow
-
Support
Account
×
Nomenclature and Contemporary Affirmation of the Unsupervised Learning in Text and Document Mining
Annaluri Sreenivasa Rao, Prof. S. Ramakrishna
Volume 15 Issue 2
Global Journal of Computer Science and Technology
Volume 15 Issue 2
Global Journal of Computer Science and Technology
Document clustering is primarily a method applied for an uncomplicated, document search, analysis and review of content or is a process of automatic classification of documents of similar type categorized to relevant clusters, in a clustering hierarchy. In this paper a review of the related work in the field of document clustering from the simple techniques of word and phrase to the present complex techniques of statistical analysis, machine learning etc are illustrated with their implications for future research work.
