An unsupervised clustering approach for leukaemia classification based on DNA micro-arrays data

Authors:
Simone Garatti;Sergio Bittanti;Diego Liberati;Andrea Maffezzoli
Affiliations:
Department of Electronics and Information, Milan Polytechinics, Piazza Leonardo da Vinci 32, 20133 Milan, Italy. E-mail: {bittanti,sgaratti}@elet.polimi.it;Department of Electronics and Information, Milan Polytechinics, Piazza Leonardo da Vinci 32, 20133 Milan, Italy. E-mail: {bittanti,sgaratti}@elet.polimi.it;Institute of Electronics, Information and Telecommunication Engineering, National Research Council, Milan, Italy. E-mail: liberati@elet.polimi.it;Department of Bioengineering, Milan Polytechnics, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
Venue:
Intelligent Data Analysis
Year:
2007

Citing 10
Cited 1

Algorithms for clustering data

Algorithms for clustering data
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Data clustering: a review

ACM Computing Surveys (CSUR)
Principles of data mining

Principles of data mining
Principal Direction Divisive Partitioning

Data Mining and Knowledge Discovery
Techniques of Cluster Algorithms in Data Mining

Data Mining and Knowledge Discovery
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Cancer classification using gene expression data

Information Systems - Special issue: Data management in bioinformatics
On the relationships between user profiles and navigation sessions in virtual communities: A data-mining approach

Intelligent Data Analysis
A comparative analysis on the bisecting K-means and the PDDP clustering algorithms

Intelligent Data Analysis

A comprehensive validity index for clustering

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

DNA micro-arrays provide thousands of genomic expressions on the same subject. A main issue is then to find the subset of genes whose degeneration is responsible of a certain type of cancer. In this paper, starting from a paradigmatic classification problem of two kinds of Leukaemia, we discuss the use of data-mining techniques in such a context. Particular attention is devoted not only to the classification method but also to all the data analysis steps including data pre-processing and information retrieval.