Fuzzy generalised classifier for distributed knowledge discovery Online publication date: Sat, 28-Jun-2014
by Raghuram Bhukya; Jayadev Gyani
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 8, No. 3, 2013
Abstract: 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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com