Data analysis

The invention relates to a method of comparing data which method comprises defining a plurality of data points in respect of each item to be compared across the complete range of data, converting each data point to a vector spatial function, said function being characteristic of the position/shape and/or relative intensity of the data at that point, assembling the vector spatial functions for the data range in question as a cluster and then determining the kernel function in respect of said cluster, determining a radial basis function for each kernel which is characteristic of all the information in that spectrum and comparing the radial base function of the cluster kernel of the sample item with the radial basis function of the cluster kernel of the other data items within the database.

The drawing of FIG. 8 is a schematic representation of a neural network, which can be adapted for use in the apparatus of the present invention. In this case, the radial basis function of the kernel of the cluster of spectral data in respect of the sample is fed into the output neurone. This information is processed by a multitude of processors in the output layer and is presented at the output of neural networks. In the example shown in FIG. 8, a single output neurone is shown as the output layer. In accordance with the present invention, a multitude of output neurones would be provided, one in respect of each sample in the database available for comparison. The processed radial basis function data is provided at each of the output neurones and is compared with the local kernel function data for the sample with the corresponding function for each microorganism spectrum within the database. The degree of similarity or overlap can be determined by using a spreading factor which characterise each cluster. An exact match or a very close match will result in a clear identification of the sample microorganism. Where there is no direct correspondence in high dimensional space between the data cluster for a sample with other data clusters in the database, then a vector will be presented detailing the clusters in high dimensional space nearest to the radial basis function of the sample. This will give an indication of the degree of similarity or overlap between the unknown sample and known similar spectra within the database. This will enable the analyst to call up the graphic data relating to the particular “close matches” and to compare them visually. It will be appreciated by the person skilled in the art that the radial basis function of the cluster in respect of a given sample in high dimensional space will be a result of all the features of each data point within each sample (spectrum) constituting the clusters of samples and that the radial basis function will be determined, spatially, by the individual values of the vector functions of each sample point in high dimensional space. Thus several similar microorganisms that are not identical may reside in the same proximate area of high dimensional space. The relative position of each sample will be determined by the extent of the differences in their spectral details. If the microorganisms are of the same genus then the two reference points defined by the spectral clusters will substantially coincide, and the greater the extent of the overlap the greater the similarity of the microorganisms. FIG. 9 is an algorithm for determining the vector function of the point in HDS for the kernel cluster of any given spectrum FIG. 10 is the detail of a computer program for performing the algorithm of FIG. 9 . As a result of Cover's theorem, a non-linear transformation might transform a complex pattern classification problem into a linearly separable one. Also by using transformations in possibility theory (fuzzification and defuzzification), uncertainty in a population of patterns will be resolved. These transformations also increase the dimensionality of pattern space which according to Cover's theorem results are desirable too.