Cooling Growing Grid: An Incremental
Self-organizing Neural Network For
Data Exploration
Author(s)
V. Tomenko & V. Popov
Abstract
Fundamental self-organizing artificial neural networks, both static (with
predefined number of neurons) and incremental, are presented and goals of
competitive learning are enumerated. A novel incremental self-organizing ANN
Cooling Growing Grid (CGG) is proposed, which combines the advantages of
static and incremental approaches and overcomes their main drawbacks. The
estimation of growth direction is made less haphazard and the weights adaptation
rule is modified in order to achieve better performance for highly nonuniform
real-world data. The main performance measures for evaluation of reference
vectors distribution are utilized in order to compare CGG with existing models.
Keywords: self-organizing neural network, incremental, data exploration.
1 Introduction
Exploratory data analysis is the data-driven search for statistical insights and
models, resulting in understandable data set representation and preservation of as
much essential information in the data, as possible. Exploratory data analysis is
used as a tool in data mining. Self-organizing ANNs are extremely important and
useful for exploratory data analysis. Their main distinctive feature is ability to
\“represent” similar in some sense input samples (real-valued vectors) by single
prototype vector. Therefore, such networks are able to extract significant features
from data sets. Furthermore, the results can be visualized in order to find groups
(clusters, categories) of similar patters and understand their mutual arrangement.
On the other hand, self-organizing networks together with auxiliary techniques
can be used to control robots, for data compression, prediction. Such wide
applicability resulted in development of different variants of self-organizing
Keywords
self-organizing neural network, incremental, data exploration.
Related Book
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