Enhanced Data Topology Preservation with Multilevel Interior Growing Self - Organizing Maps
نویسندگان
چکیده
This paper presents a novel architecture of SOM which organizes itself over time. The proposed method called MIGSOM (Multilevel Interior Growing Self-Organizing Maps) which is generated by a growth process. However, the network is a rectangular structure which adds nodes from the boundary as well as the interior of the network. The interior nodes will be added in a superior level of the map. Consequently, MIGSOM can have three-Dimensional structure with multi-levels oriented maps. A performance comparison of three Self-Organizing networks, the Kohonen feature Map (SOM), the Growing Grid (GG) and the proposed MIGSOM is made. For this purpose, the proposed method is tested with synthetic and real datasets. Indeed, we show that our method (MIGSOM) improves better performance for data quantification and topology preservation with similar map size of GG and SOM.
منابع مشابه
A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps
Keywords: Topology preserving Self-organizing map Growing cell structures Visualization methods Delaunay triangulation The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsuper...
متن کاملGrowing a hypercubical output space in a self-organizing feature map
Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective stru...
متن کاملSelf-Organizing Feature Maps with Self-Organizing Neighborhood Widths
Self-organizing feature maps with self-determined local neighborhood widths are applied to construct principal manifolds of data distributions. This task exempli es the problem of the learning of learning parameters in neural networks. The proposed algorithm is based upon analytical results on phase transitions in self-organizing feature maps available for idealized situations. By illustrative ...
متن کاملRelations between generalized fractal dimensions and Kohonen's self-organizing map
In this paper we present a method to improve the learning results for diierent data sets, which earlier were diicult or impossible to learn, and to criticize the map's development during learning. Calculating the generalized fractal dimensions of the data set we choose the map's dimension accordingly for guaranteeing the maps ability of topology preservation. Furthermore we explore diierent sta...
متن کاملData Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps
The Self Organizing Maps (SOM) is regarded as an excellent computational tool that can be used in data mining and data exploration processes. The SOM usually create a set of prototype vectors representing the data set and carries out a topology preserving projection from high-dimensional input space onto a low-dimensional grid such as two-dimensional (2D) regular grid or 2D map. The 2D-SOM tech...
متن کامل