Abstract:  One of the major problems in spectral co-clustering analysis is the determination of the number of clusters in datasets，which is a basic input for most spectral co-clustering algorithms, In this paper, we propose.new method for automatically estimating the number of clusters in datasets and modify spectral co-clustering
documents and words, which is based on an existing algorithm for visual assessment of tendency(VAT)of a data set，using several common image and signal processing techniques.The method determining the number of clusters includes three main steps. First，the input matrix generated by spectral co-clustering documents and words is created into reordered dissimilarity gray image, from the image it is better able to highlight the potential cluster structure in dataset. We generate gray image use the VAT algorithm.Then, sequential image processing operations are used to segment the regions of interest in the gray image and to convert the filtered image into a distance- transformed image.There processing operations consist of gray morphology, image binarization, distance
transform. Finally, we project the transformed image onto the diagonal axis，which yields a one-dimensional signal，from which we can extract the number of clusters by major peaks and valleys after smoothing signal. When the number of clusters is determined，we modify the typical spectral co-clustering algorithm.The primary difference between typical spectral co-clustering algorithm and our method for spectral co-clustering documents and words based on morphology is that the number of clusters is set by user before the algorithm in typical algorithm, but user may not know the true value of it，if that is the case, the value maybe set incorrectly, incorrect parameter settings may lead to inaccurate clustering. Automatic determination of the cluster number in our proposed algorithm also
helps to improve the clustering results. Our experimental results show that the proposed method works well in practice.