Name
Abstract | ||
|---|---|---|
Over time, it has been found there is valuable information within the data sets generated into different areas. These large data sets required to be processed with any data mining technique to get the hidden knowledge inside them. Due to nowadays many of data sets are integrated with a big number of instances and they do not have any information that can describe them, is necessary to use data mining methods such as clustering so it can permit to lump together the data according to its characteristics. Although there are algorithms that have good results with small or medium size data sets, they can provide poor results when they work with large data sets. Due to above mentioned in this paper we propose to use different cluster validation methods to determine clustering quality, as its analysis, so at the same time to determine in an empiric way the more reliable rates for working with large data sets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MICAI.2013.30 | MICAI (Special Sessions) |
Keywords | Field | DocType |
medium size data set,large data,data mining method,large data set,large data sets,cluster validation indexes,different cluster validation method,valuable information,different area,clustering quality,data mining technique,data analysis,data mining | Data mining,Data set,Pattern recognition,Validation methods,Computer science,Pattern clustering,Artificial intelligence,Cluster analysis,Statistical classification,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Santibáñez Sánchez | 1 | 0 | 0.34 |
| R. M. Valdovinos | 2 | 193 | 13.67 |
| A. Trueba | 3 | 1 | 0.71 |
| Erendira Rendón | 4 | 13 | 1.92 |
| R. Alejo | 5 | 158 | 10.40 |
| E. López | 6 | 0 | 0.34 |