Clustering on Wavelet and Meta
Clustering on Wavelet and Meta

papers in PDF format
papers in PDF format

... relocation continues until the criterion function, e.g. square-error converges. Despite its wide popularity, k-means is very sensitive to noise and outliers since a small number of such data can substantially influence the centroids. Other weaknesses are sensitivity to initialization, entrapments in ...
IOSR Journal of Computer Engineering (IOSR-JCE)
IOSR Journal of Computer Engineering (IOSR-JCE)

Clustering Techniques
Clustering Techniques

strategies of clustering for collaborative filtering
strategies of clustering for collaborative filtering

A Rough Set based Gene Expression Clustering Algorithm
A Rough Set based Gene Expression Clustering Algorithm

... Clustering gene expression data: Clustering is one of the first steps in gene expression analysis. One of the important characteristics of gene expression data is that it is meaningful to cluster both genes and samples. During cluster analysis, genes are clustered based on similarity. Proximity meas ...
Introduction to clustering techniques - IULA
Introduction to clustering techniques - IULA

PV2326172620
PV2326172620

Knowledge Discovery to Analyze Student Performance using k
Knowledge Discovery to Analyze Student Performance using k

Association Rules - Personal Web Pages
Association Rules - Personal Web Pages

ID2313791384
ID2313791384

Supervised Learning and k Nearest Neighbors
Supervised Learning and k Nearest Neighbors

Comparative Analysis of EM Clustering Algorithm and Density
Comparative Analysis of EM Clustering Algorithm and Density

Data Mining, Chapter - VII [25.10.13]
Data Mining, Chapter - VII [25.10.13]

Data Mining
Data Mining

IJARCCE 12
IJARCCE 12

Machine Learning with Spark - HPC-Forge
Machine Learning with Spark - HPC-Forge

- Krest Technology
- Krest Technology

k-nearest neighbor algorithm
k-nearest neighbor algorithm

Document
Document

... 64-dimensional space ...
slides
slides

Data Clustering - An Overview and Issues in Clustering Multiple
Data Clustering - An Overview and Issues in Clustering Multiple

Unsupervised Learning: Clustering
Unsupervised Learning: Clustering

MIS2502: Data Analytics Clustering and Segmentation Jing Gong
MIS2502: Data Analytics Clustering and Segmentation Jing Gong

Cluster
Cluster

Nearest-neighbor chain algorithm



In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.