TY - CHAP
T1 - Robust self-organizing maps
AU - Allende, Hector
AU - Moreno, Sebastian
AU - Rogel, Cristian
AU - Salas, Rodrigo
PY - 2004
Y1 - 2004
N2 - The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.
AB - The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.
KW - Artificial neural networks
KW - Data mining
KW - Robust learning algorithm
KW - Self organizing maps
UR - http://www.scopus.com/inward/record.url?scp=25144443432&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30463-0_22
DO - 10.1007/978-3-540-30463-0_22
M3 - Chapter
AN - SCOPUS:25144443432
SN - 3540235272
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 179
EP - 186
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Sanfeliu, Alberto
A2 - Martinez-Trinidad, Jose Francisco
A2 - Carrasco-Ochoa, Jesus Ariel
PB - Springer Verlag
ER -