A new method for analysis of long-time series("large" data)is suggested.The method generalizes the well-known detrended fluctuation analysis(DFA)approach [1] and uses the scaling properties of the beta-distribution function.This new method allows finding the stable parameters and reducing the series containing 105 and more data points to analysis of 10-20 stable parameters,only.The new procedure of clusterization with the usage of the generalized Pearson correlation function allows taking into account the influence of different factors and combine/separate different parameters into a statistical cluster with respect to the qualitative external parameters considered.The most interesting feature of the proposed approach is that it admits the secondary fit of the reduced parameters that were calculated in the results of the first fitting procedure.The proposed method is rather flexible and general and can be applied to a wide set of large data.As an example the membrane currents containing 250000 data points characterizing the current of “control” series belonging to different biologic cells are considered.In the results of application of the method it becomes possible to realize the essential reduction of the initial long-time series and obtain 20 stable parameters that admit the further clusterization in accordance with influence of some qualitative factor.In our case this factor coincides with currents recorded from living cells and empty electrodes,when the presence of biological material was absent.The definite separation of these long-time series in terms of the reduced parameters from the background noise,i.e.current referring to disconnected equipment was obtained.This method opens new possibilities in creation of a reduced database(specific fingerprints)of different long-time series for their comparison and subsequent analysis.We think that the fluctuation spectroscopy based on beta-distribution(FSBD)function is applicable to a wide set of "large data",where the clearly expressed trend is absent and the urgent necessity of reduction of these data for their further comparison exists.