Self-Organizing Feature Maps in Correlating Groups of Time Series: Experiments with Indicators Describing Entrepreneurship
نویسندگان
چکیده
In the paper, we briefly describe a problem of identification of entrepreneurship determinants with respect to economic development of countries. In order to solve this problem, we need to identify correlations between entrepreneurship and macroeconomic indicators. The main attention in the paper is focused on selecting a proper computer tool for solving this problem. As a tool supporting identification, SelfOrganizing Feature Maps (SOMs) have been chosen. Some modification of the clustering process using SOMs is proposed by us to improve classification results and efficiency of the learning process. At the end, we indicate some challenges of further research.
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 کاملData Mining and Knowledge Discovery with Emergent Self-Organizing Feature Maps for Multivariate Time Series
Self-Organizing Feature Maps, when used appropriately, can exhibit emergent phenomena. SOFM with only few neurons limit this ability, therefore Emergent Feature Maps need to have thousands of neu-rons. The structures of Emergent Feature Maps can be visualized using U-Matrix Methods. U-Matrices lead to the construction of self-organzing classiiers possessing the ability to classify new datapoint...
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 کاملNon-Linear Time Series Modeling with Self-Organization Feature Maps
A locally linear approach based on Kohonen self-organizing feature mapping (SOFM) is proposed for the modeling of non-linear time series. This approach exploits the neighborhood preserving property of Kohonen feature maps. The key difference is that the local model fitting is performed directly over a matched neighborhood of the constructed SOFM neural field. The initial results show that this ...
متن کاملLandforms identification using neural network-self organizing map and SRTM data
During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...
متن کامل