A self-organizing map for analysis of high-dimensional feature spaces with clusters of highly differing feature density
نویسندگان
چکیده
منابع مشابه
Steel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps
Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...
متن کاملA hierarchical self-organizing feature map for analysis of not well separable clusters of different feature density
This paper introduces a hierarchical Self-Organizing Feature Map (SOFM). The partial maps consist of individual numbers of neurons, which makes a cluster analysis with di erent degrees of resolution possible. A de nition of a special Mahalanobis space of the data set during the learning improves the properties concerning clusters with low density.
متن کاملUncovering the Hierarchical Structure of Text Archives by Using an Unsupervised Neural Network with Adaptive Architecture
Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires the development of stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In this paper we present the Growing Hierarchical Self-Organizing Map (GH-SOM), a neural network model based on the self-organizing map. The main f...
متن کاملThe Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملTowards Growing Self-Organizing Neural Networks with Fixed Dimensionality
The competitive learning is an adaptive process in which the neurons in a neural network gradually become sensitive to different input pattern clusters. The basic idea behind the Kohonen’s Self-Organizing Feature Maps (SOFM) is competitive learning. SOFM can generate mappings from high-dimensional signal spaces to lower dimensional topological structures. The main features of this kind of mappi...
متن کامل