---
title: "Distributed Data Mining: Why Do More Than Aggregating Models"
authors:
  - "Mohamed Aounallah"
  - "Guy W. Mineau"
doi: ""
url: "https://ijcai.org/proceedings/2007/425"
pdf: "https://ijcai.org/proceedings/2007/0425.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2645-2649"
track: ""
keywords:
---

# Distributed Data Mining: Why Do More Than Aggregating Models

Mohamed Aounallah, Guy W. Mineau

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2645-2649.

## Abstract

In this paper we deal with the problem of mining large distributed databases. We show that the aggregation of models, i.e., sets of disjoint classification rules, each built over a subdatabase is quite enough to get an aggregated model that is both predictive and descriptive, that presents excellent prediction capability and that is conceptually much simpler than the comparable techniques. These results are made possible by lifting the disjoint cover constraint on the aggregated model and by the use of a confidence coefficient associated with each rule in a weighted majority vote.