1. A data compressing apparatus for diagnosing machine characteristics of a construction machine so as to reduce costs of communication between the construction machine and an external unit, comprising: detecting sensors which detect a plurality of data sets from the construction machine, each of the plurality of data sets including n parameter values that vary according to an operation of the construction machine, where n is a natural number; compressing means for compressing the plurality of data sets into neuron model parameters so as to reduce costs of communication between the construction machine and the external unit, where the neuron model parameters are representative of the machine characteristics; and sending means for sending the neuron model parameters to the external unit, wherein said compressing means compresses the plurality of data sets by: inputting said plurality of data sets detected by said detecting sensors into an n-dimensional space; arranging a predetermined number of neurons which are smaller in number than the plurality of data sets in the n-dimensional space, where the predetermined number is set in accordance with the reduced cost of communication between the construction machine and the external unit; carrying out unsupervised learning of a neural network on the neurons to obtain a neuron model; determining one neuron of the neuron model as a winning neuron for each of the plurality of data sets which one neuron has the shortest distance to each of said plurality of data sets; and calculating the neuron model parameters by use of the winning neurons, wherein the neuron model parameters include, for each of the winning neurons, coordinate data representing coordinates of each said winning neuron, average distance data representing an average of distances of each said winning neuron to the plurality of data sets, and weight data representing how many times each said winning neuron has been determined as a winning neuron.