Finding of similar textual documents in very large amount of data is very complicated problem because of complexity O(n2). In order to find the similar documents, there are needed many comparison iterations of pairs of textual documents. Using proximity sensitive compression algorithms, the comparison of two documents is very simplified and in the same time, the comparison execution time is shorter. SimHash and MinHash algorithms for compression enable preparation of text data sets (summaries) which have proximity sensitive properties. SimHash data set (summary) is based on the distribution of words inside the text document. MinHash data set (summary) is based on probability of words occurrence inside the text document. Instead of text items comparison, the main idea in this kind of data mining in large number of text documents is comparison of data sets (summaries). In order to additionally decrease the execution time, in parallel with SimHash and MinHash, there is the third algorithm used, LSH algorithm. The base of LSH algorithm is the definition of candidates for each text document. The candidates are the text documents, which have similarity level, at least s. Similarity s is lower of similarity, which are identified later, in the process of text document comparison. Using such approach, only the text documents, which are defined as candidates, are used in further steps of comparison of data sets (summaries), instead of comparison of data sets (summaries) of all input text documents. The execution time needed for finding similar text documents in very large amount of data is decreased from couple of hours to 1 minute using proximity sensitive compression algorithms, MinHash and SimHash in combination with LSH algorithm for definition of candidates.