Tree Representation: Knowledge Discovery from Uncertain Data

Abstract

Most of the real-world data is gathered locally, organized regionally leading to distributed environment. The analysis of such data will assist decision makers for promoting their business. Most of the data mining strategies are multi-pass techniques employed for mining and discovering the knowledge. Hence, the paper focuses on developing two pass data mining constructs to handle uncertain data. One of the simplest and feasible item-pattern representations is transactional tree. The developed methodology for constructing transactional trees is studied in detail and apt technological solutions is proposed. It is proposed to construct transactional tree from uncertain data which is usually associated with existential probabilities and to establish distributed computing environment for developing transactional trees. This paper aims at bringing out the knowledge valid for a given location through which it is expected to derive global knowledge.