A Fourier Analysis Based Approach to Learning Decision Trees in a Distributed Environment
Byung Hoon Park, Rajeev Ayyagari, Hillol Kargupta · 2001
Spurred by advances in communication technologies, mobile computing and databases that are distributed have become widespread. Such a computing environment involves data that is stored at geographically dispersed locations, and the so-called “slim” computing devices such as palmtops and wearable computers. The decentralized nature of data storage and this new paradigm in computing give rise to several issues, such as security, communication overhead, computational load demands and scalability, that are not adequately addressed by traditional centralized data mining techniques. It is essential that algorithms designed for distributed data mining scenarios mitigate some of these issues. This paper attempts to adapt one centralized data mining technique, decision tree learning, to such an environment. It presents a scalable algorithm that can be used to build decision trees from a distributed, heterogeneous database while minimizing communication overheads. This paper also shows how a decision tree may be represented in terms of its Fourier spectrum. It uses this Fourier spectrum based technique to aggregate decision trees built at the various distributed sites, simplifying the model built during the data mining stage, and notes some additional advantages of the Fourier spectrum approach.