SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH
Abstract & Details
Research Area
Computer Engineering
Keywords
clustering
k-Nearest Neighbor
various-width clustering
high dimensional
Abstract
Over recent decades, database sizes have grown large. Due to the large sizes it create new challenges, because many machine learning algorithms are not able to process such a large volume of information. The k-nearest neighbor (k-NN) is widely used in many machines learning problem. k-NN approach incurs a large computational cost. In this research a k-NN approach based on various-width clustering is presented. The k-NN search technique is based on VWC is used to efficiently find k-NNs for a query object from a given data set and create clusters with various widths. This reduces clustering time in addition to balancing the number of produced clusters and their respective sizes.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Aware Sneha Nandlal | Matoshri College of Engineering & Research Centre, Nashik |
| 2 | Varsha H. Patil | Matoshri College of Engineering & Research Centre, Nashik |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Nandlal, Aware Sneha & Patil, Varsha H. (2017). SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH. International Journal of Advance Research and Innovative Ideas In Education, 3(2), 443-447.
MLA Style
Nandlal, Aware Sneha, and Varsha H. Patil. "SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH." International Journal of Advance Research and Innovative Ideas In Education, vol. 3, no. 2, 2017, pp. 443-447.
IEEE Style
Aware Sneha Nandlal and Varsha H. Patil, "SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH," International Journal of Advance Research and Innovative Ideas In Education, vol. 3, no. 2, pp. 443-447, 2017.
Vancouver Style
Nandlal Aware Sneha, Patil Varsha H.. SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH. International Journal of Advance Research and Innovative Ideas In Education. 2017;3(2):443-447.
Harvard Style
Nandlal, Aware Sneha & Patil, Varsha H. (2017) 'SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH', International Journal of Advance Research and Innovative Ideas In Education, 3(2), pp. 443-447.
Chicago Style
Nandlal, Aware Sneha and Varsha H. Patil. "SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH." International Journal of Advance Research and Innovative Ideas In Education 3, no. 2 (2017): 443-447.
Turabian Style
Nandlal, Aware Sneha and Varsha H. Patil. "SURVEY ON VARIOUS-WIDTH CLUSTERING FOR EFFICIENT k-NEAREST NEIGHBOR SEARCH." International Journal of Advance Research and Innovative Ideas In Education 3, no. 2 (2017): 443-447.
Related Research
Comprehensive Review of Existing Chatbot Systems for Career Assistance, Resume Support, and ATS-Aware Guidance
PDF Unavailable
Development of an AI-Powered Multimodal Web Assistant with Intelligent Resume Building and ATS Enhancement
PDF Unavailable
A Deep Learning-Based Framework for Mood-Oriented Music Recommendation Using Facial Expression Analysis
PDF Unavailable
Survey On : Intelligent Payroll and Human Resource Management Systems: A Systematic Review of Automation, Security, and Analytics
PDF Unavailable
Civic Engagement & Empowerment Platform
PDF Unavailable
Recent Developments in Microneedle Technology and Its Diverse Biomedical Applications
PDF Unavailable
RAG System Development with Pydantic AI ChromaDB & Groq
PDF Unavailable
Machine Learning Based Early Stage Diabetes Detection System
PDF Unavailable
A Survey on Skillsense:AI Career Analyzer App
PDF Unavailable
Employee Performance Portal
PDF Unavailable