Conceptual modeling rules extracting for data streams

Abstract

Data take the form of continuous data streams rather than traditional stored databases in a growing number of applications, including network traffic monitoring, network intrusion detection, sensor networks, fraudulent transaction detection, financial monitoring, etc. People are interested in the potential rules in data streams such as association rules and decision rules. Compared with much work on developing algorithms of data streams mining, there is little attention paid on the modeling data mining and data streams mining. Considering the problem of conceptual modeling data streams mining, we put forward a data streams oriented decision logic language as a granular computing formal approach and a rules extracting model based on granular computing. In this model, we propose the notion of granular drifting, which accurately interpret the concept drifting problem in data streams. This model is helpful to understand the nature of data streams mining. Based on this model, new algorithms and techniques of data streams mining could be developed.