Name
Abstract | ||
|---|---|---|
Clustering of real-world datasets is a complex problem. Optimization models seeking to maximize a fitness function assume that the solution corresponding to the global optimum is the best clustering solution. Unfortunately, this is not always the case, mainly because of noise or intrinsic ambiguity in the data. In this work we present a set of tools implementing classical and novel techniques to approach clustering in a systematic way, with an application example to a complex biological dataset. The tools deal with the problem of generating multiple clustering solutions, performing cluster analysis on such clusterings (i.e. Meta Clustering) and reducing the final number of clusterings by the appropriate application of different Consensus techniques. A subsequent crossing of prior knowledge to the obtained clusters helps the user in better understanding its meaning and validates the solutions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02282-1_21 | WILF |
Keywords | Field | DocType |
different consensus technique,tools deal,complex biological dataset,clustering solution,appropriate application,meta clustering,consensus algorithms,multiple clustering solution,interactive data analysis,cluster analysis,complex problem,application example,data visualization,consensus clustering,fitness function,data analysis | Data mining,Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Consensus clustering,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
5571 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ida Bifulco | 1 | 16 | 3.54 |
| Carmine Fedullo | 2 | 7 | 1.57 |
| Francesco Napolitano | 3 | 61 | 5.16 |
| Giancarlo Raiconi | 4 | 118 | 15.08 |
| Roberto Tagliaferri | 5 | 428 | 55.64 |