an adaptive k-means clustering algorithm and its application to face recognition
Clicks: 207
ID: 237949
2010
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
207 views
33 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #13 of 22 articles by views in current eye research
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Pattern recognition is an emerging research area that studies the operation and design of systems that recognize patterns in data. Clustering is an essential and very frequently performed task in pattern recognition and data mining.Clustering refers to the process of grouping samples so that the samples are similar within each group. The groups are called clusters. k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given dataset of n points i x through a certain number of clusters fixed apriori. The difficulty in implementing k-means method for a large database is in determining the number of clusters which has to be randomly chosen. To overcome this difficulty, we propose a variation of the k-means algorithm, where the number of clusters ‘k’ can change dynamically depending on the data points and a threshold value given as an input. The proposed algorithm is applied in face recognition which is a very complex form of pattern recognition .It is used to verify whether a test face belongs to the database of faces and if so, identifies it.
| Reference Key |
rajeswari2010journalan
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;N. Rajeswari;B. Thilaka;K. Rajalakshmi |
| Journal | current eye research |
| Year | 2010 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.