Fuzzy generalised classifier for distributed knowledge discovery

摘要

Classifier building form distributed data sources has been a fundamental computational problem to realise distributed knowledge discovery. The classification rule extraction from distributed databases suffers from the problems of high communication cost, lack of interpretability of rules and poor performance in handling high categorical data. The aim of this paper is to extend fuzzy generalised association rule extraction technique which is well proved in handling such issues to extract classification rules from distributed datasets. This paper presents a distributed data driven fuzzy generalised associative classifier (D3FGAC) framework for distributed knowledge discovery which extracts data driven fuzzy generalisation rules from horizontally fragmented datasets with minimum communication cost and builds global compact classifier using extracted rules. The experiments conducted on UCI datasets and their comparisons to other existing model shown in article to prove the efficiency of proposed framework.