1. A knowledge finding method in data mining for extracting higher-order knowledge by reading data, sampled from a database stored in a large-capacity data storage device, into a main memory of a calculating machine, comprising: input means for inputting a learning algorithm and data on which said learning algorithm performs learning; learning means for inputting plural partial data sets, generated by sampling from data stored in said main memory, as training data to said learning algorithm, and causing said algorithm to perform learning on said data so as to obtain plural hypotheses; data selection means for predicting function values for plural sample candidate points read from said large-capacity data storage device by using the plural hypotheses obtained by said learning means, estimating information amounts of the candidate points based on obtained predicted values, selecting one or plural candidate points in large information amounts, and additionally storing the selected candidate points into said main memory; control means for repeating processing of selective data storage and knowledge finding by using said learning means and said data selection means until a predetermined stoppage condition is satisfied, and storing resulted plural hypotheses as final hypotheses into said main memory; and prediction means for predicting a label value of unknown-labeled data inputted into said input unit, with an average or weighted average among said plural hypotheses.