Dynamic Knowledge Acquisition from Complex Data with Rough Sets

Abstract

This dissertation is concerned with knowledge discovery from complex data in dynamic environments, where complex data refer to different types of object features and information sources, and dynamicity implies the variation of the objects, their features and of the feature values over time. Within the framework of rough sets, knowledge refers to the ability to classify data in the form of “IF-THEN” decision rules, which may change with the variation of the complex data in dynamic environments. For four kinds of typical complex data, rough sets-based incremental algorithms for dynamic knowledge acquisition are proposed and discussed: (1) Probabilistic set-valued data have set-values with a probability distribution. Considering addition and deletion of features in probabilistic set-valued data, an extended variable precision rough set model is built based on a novel binary relation, and matrix-based incremental algorithms are developed to build and update IF-THEN rules. (2) Fuzzy data are feature values as indicated by fuzzy memberships. In terms of the simultaneous variation of objects and their features described by fuzzy data, a matrix representation of rough fuzzy sets is presented by defining a relation matrix associated with a novel matrix operator. A corresponding incremental method to update IF-THEN rules is proposed, which can update multiple relation matrix entries. (3) Multi-source hybrid data originate from different information sources and have multiple types of features. Taking the simultaneous variation of objects, features and feature values in multi-source hybrid data into account, a novel multi-source composite rough set model is proposed by integrating multiple binary relations and a matrix-based incremental method to update IF-THEN rules, which can avoid the disclosure of decision rules. (4) Multiple-source interval-valued data are collected from different information sources. Considering addition and deletion of sources for multi-source interval-valued data, incremental fusion mechanisms are discussed and suitable incremental fusion algorithms are developed, which can reduce ambiguities and uncertainties in the data and improve the quality of the knowledge acquired. Theoretical analyses and experiments are conducted to verify the efficiency of the methods proposed. This study is beneficial to analyze the uncertainty problems of complex data due to their variety and veracity, and provides new methodologies for dynamic knowledge acquisition from complex data. Furthermore, the incremental methods presented can improve the processing speed of big data.