In this work the classification of Force Expiratory volume in 1 second (FEV 1) in pulmonary function test is carried out using Spirometer and Self Organizing Feature Map Algorithm. Spirometry data are measure with flow volume spirometer from subject (N=100 including Noramal, and Abnormal) using standard data acquisition protocol. The acquire data are then used to classify FEV1. Self Organizing ...

In exploratory analysis of high-dimensional data the self-organizing map can be used to illustrate relations between the data items. We have developed two measures for comparing how diierent maps represent these relations. The other combines an index of discontinuities in the mapping from the input data set to the map grid with an index of the accuracy with which the map represents the data set...

The self-organizing map is discussed as an unsupervised clustering method. Its ability to form clusters indicates similar features in a data set. Based on this property, it is demonstrated that a self-organizing map is capable of identifying features within software code by grouping procedures with similar properties together. This allows us to identify potential objects, abstract data types or...