Text mining from large scaled data is of great importance in computer sci- ence. In this paper, we consider fundamental problems on text mining from compressed strings, i.e., computing a longest repeating substring, longest non-overlapping repeat- ing substring, most frequent substring, and most frequent non-overlapping substring from a given compressed string. Also, we tackle the following novel problem: given a compressed text and compressed pattern, compute the representative of the equiva- lence class of the pattern w.r.t. the text. We present algorithms that solve the above problems in time polynomial in the size of input compressed strings. The compression scheme we consider is straight line program (SLP) which has exponential compres- sion, and therefore our algorithms are more efficient than any algorithms that work on uncompressed strings.