Behaviors of Lazy Self-Organizing MapConsidering Lazy-Neuron Rate
Taku Haraguchi, Haruna Matsushita, Yoshifumi Nishio
Dept. Electrical and Electronic Eng.,
Tokushima University
Email: {taku, haruna, nishio}@ee.tokushima-u.ac.jp
S UMMARYIn the real world, the amount and the complexity of data increase from year to year. Therefore, it is important to classify
various data exactly. In data mining, clustering is one of typical analysis techniques and is studied for many applications, suchas a statement, a pattern recognition, an image analysis and so on. Then, the Self-Organizing Map (SOM) [1] has attracted
attention for the study on clustering [2] in recent years. The SOM is an unsupervised neural network introduced by Kohonen
in 1982 and is a simplified model of the self-organization process of the brain. The SOM can retain topological feature, whichis association between neurons, and the SOM can classify similar data.
In the previous study, we have applied the ant world to the conventional SOM. There is a fascinating report that about
20% of worker ants are “lazy” [3], however, its report doesn’t be established definite reason and assuredness. Furthermore,
researchers reported its report think the lazy ants have some rules. Additionally, in the simulation result, there also is anotherreport that the ants group, which contains the lazy ants at food collections, can collect more foods than the group whichcontains only the worker ants. From these reports, we have proposed a new type of SOM algorithm, which is called Lazy
SOM (LSOM) algorithm [4]. The important feature of the LSOM is that three kinds of neurons exist; worker neurons, lazyneurons, which do not work, and indecisive neurons which are the neighborhoods of the lazy neurons. The learning rateof the lazy neurons is smaller than that of the worker neurons. The learning rate of the indecisive neurons becomes small
due to the lazy neurons. The learning rate of the previous LSOM depends on each neuron’s character. For this reason, theprevious LSOM can obtain the map reflecting the distribution state of the input data more effectively than the conventional
SOM, however, it tends to obtain a strongly twisted map.
Therefore, we proposed an improved LSOM [5], which has the feature of the conventional LSOM and resemble the featureof the conventional SOM. The learning rate of this improved LSOM depends on each neuron’s character and lazy-neuron rate,and decreases monotonically with the learning step. We investigated efficacy of the lazy-neuron rate of the improved LSOM
and apply it to various input data set. We confirmed that the improved LSOM containing the lazy neurons, which is from 10%
to 20% of the total, can obtain the most effective and exact map reflecting the distribution state of the input data than theconventional SOM.
In this study, we investigate the efficacy of the improved LSOM, not for feature extraction but for clustering. We apply theimproved LSOM to some input data having multiple clusters and investigate the behaviors of the improved LSOM.
R EFERENCES[1] T. Kohonen, Self-Organizing Maps, Berlin, Springer, vol. 30, 1995.
[2] J. Vesanto and E. Alhoniemi, “Clustering of the Self-Organizing Map,” IEEE Trans. Neural Networks, vol. 11, no. 3, pp. 586–600, 2002.
[3] H. Hasegawa, “Optimization of GROUP Behavior,” Japan Ethological Society Newsletter, no. 43, pp. 22–23, 2004.
[4] T. Haraguchi, H. Matsushita and Y. Nishio, “Lazy Self-Organizing Map and its Behaviors, Proc. of IJCNN’08, June 2008.
[5] T. Haraguchi, H. Matsushita and Y. Nishio, “Lazy Self-Organizing Map Considering Lazy-Neuron Rate for Effective Self-Organization, Proc. of NOLTA’08,
September 2008 (accepted).

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