Here we present a new method which detects and identifies neurons participating in assemblies, by combining the accretion method [7] with data mining approaches, in particular frequent item set mining (FIM). Thereby spike synchrony among groups of neurons is detected by the accretion approach: pairs of spike trains are tested for significant correlation and then reduced to new point processes containing only synchronized spikes. These processes are in turn correlated with single neuron spike trains and so on, until the maximal order of correlation is found. FIM algorithms help to fast and efficiently search the space of all neuronal subsets. However, FIM algorithms typically rely on a minimum support criterion to prune the search, since it guarantees soundness. In our framework, this criterion is not useful, since existence of higher correlation does not necessarily imply a frequent occurrence of spike patterns. We rather aim at selecting spike patterns that occur significantly more often than predicted by the spike and (lower order) coincidence rates. Therefore we employ the χ² measure [7] in combination with the FIM algorithm still enabling to processes large data sets very efficiently. Using simulations of massively parallel spike processes that may contain various constellations of correlated activity (e.g., as A1, A3, A2 in figure 1; top panel) we illustrate that our algorithm is well suited for detecting and identifying groups of neurons exhibiting higher correlations. In particular, the approach is able to detect and separate neuronal groups of overlapping assemblies, irrespective of the number of assemblies individual neurons are participating in. The extracted assemblies are visualized by graphs where the nodes represent neurons and edges significant pairwise correlation (Figure 1, bottom panel). Thus we provide a powerful tool for the detection and identification of correlated groups of neurons.