Yoking of Algorithms for Effective Clustering

Effective Clustering Algorithms for

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in Bioinformatics research. In this paper, K-Means algorithm hybridised with Cluster Centre Initializa...

Effective Clustering Algorithms for Gene Expression Data

Effective Term Based Text Clustering Algorithms

Text clustering methods can be used to group large sets of text documents. Most of the text clustering methods do not address the problems of text clustering such as very high dimensionality of the data and understandability of the clustering descriptions. In this paper, a frequent term based approach of clustering has been introduced; it provides a natural way of reducing a large dimensionalit...

Effective Spell Checking Methods Using Clustering Algorithms

This paper presents a novel approach to spell checking using dictionary clustering. The main goal is to reduce the number of times distances have to be calculated when finding target words for misspellings. The method is unsupervised and combines the application of anomalous pattern initialization and partition around medoids (PAM). To evaluate the method, we used an English misspelling list co...

Effective Clustering Algorithms for VLSI Circuit Partitioning Problems

In this article, the effective circuit partitioning techniques are employed by using the clustering algorithms. The technique uses the circuit netlist in order to cluster the circuit in partitioning steps and it also minimizes the interconnection distance with the required iteration level. The clustering algorithm like K-Mean, Y-Mean, K-Medoid are performed on the standard benchmark circuits. T...