Name
Title | ||
|---|---|---|
An unsupervised clustering approach for leukaemia classification based on DNA micro-arrays data |
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.3233/IDA-2007-11205 | Intell. Data Anal. |
Keywords | Field | DocType |
dna micro-arrays,dna micro-arrays data,classification method,main issue,data-mining technique,genomic expression,particular attention,leukaemia classification,unsupervised clustering approach,information retrieval,data analysis step,paradigmatic classification problem,certain type,data analysis,data mining | Clustering high-dimensional data,Pattern recognition,Expression (mathematics),Computer science,Artificial intelligence,Conceptual clustering,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 2 | 1088-467X |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Garatti | 1 | 259 | 22.00 |
| Sergio Bittanti | 2 | 219 | 74.16 |
| Diego Liberati | 3 | 38 | 5.45 |
| Andrea Maffezzoli | 4 | 1 | 0.35 |