Abstract not found

The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM constructs better mappings than the classic SOM in terms of both the topological and the quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those by the SOM.

Abstract:- This paper presents an automatic parameters adjustment learning algorithm for self-organizing maps having growing ring topology. Like the existing SOM-like algorithm, the heuristic algorithm possesses many of these advantages of a good heuristic for the TSP solution. These advantages are easy implementation, fast computation, and production of good solutions. Computer programs developed in MATLAB for the heuristic algorithm were used to solve twelve test problems of the TSP from the TSPLIB. The experiment is showed that the results have an average of 2.4925 % difference from the optimum route. We can find almost optimal route by the heuristic algorithm. The algorithm is well suited for larger instances of the TSP since it has a fast convergence and low complexity. Key-Words:- Neural networks; Self-organizing maps; Traveling salesman problem; Combinatorial optimization; Neural computation 1

The Self-Organizing Map (SOM) [11, 15] is a method for mapping data relationships and distributions in high dimensions to lower dimensions, where they are easier to visualize and process. One of the problems with the SOM is that it needs an externally applied annealing scheme to learn mappings. There is no firm theoretical guidance for selecting annealing schemes and their parameters, something that must therefore be done by trial-and-error which can consume considerable effort, as any of the many parameters that govern the annealing scheme are dependent on the application and can have large influences on the outcome [19]. In addition the SOM has problems with non-uniformly distributed input spaces, as we will demonstrate in this paper. The PLSOM solves some of the problems associated with applying SOMs to non-uniformly distributed input spaces and eliminates the need for an externally enforced annealing scheme. This paper explores the performance of the Parameter-Less SOM (PLSOM) when mapping a normal distributed input space, as compared to two common versions of the ordinary SOM.