Distributed mining of classification rules

Many successful data-mining techniques and systems have been developed. These techniques usually apply to centralized databases with less restricted requirements on learning and response time. Not so much effort has yet been put into mining distributed databases and real-time issues. In this paper, we investigate issues of fast-distributed data mining. We assume that merging the distributed databases into a single one would either be too costly (distributed case) or the individual fragments would be non-uniform so that mining only one fragment would bias the result (fragmented case). The goal is to classify the objects O of the database into one of several mutually exclusive classes C i . Our approach to make mining fast and feasible is as follows. From each data site or fragment db k , only a single rule r ik is generated for each class C i . A small subset {r i1 , …, r ih } of these individual rules is selected to form a rule set R i for each class C i . These rule subsets represent adequately the hidden knowledge of the entire database. Various selection criteria to form R i are discussed, both theoretically and experimentally.