Abstract

Distributed data mining (DDM) deals with the problem of analyzing distributed, possibly multi-party data by paying attention to the computing, communication, storage, and human factors-related issues in a distributed environment. Unlike the conventional o#-the-shelf centralized data mining products, DDM systems are based on fundamentally distributed algorithms that do not necessarily require centralization of data and other resources. DDM technology is finding increasing number of applications in many domains. Examples include data driven pervasive applications for mobile and embedded devices, grid-based large scale scientific and business data analysis, security and defense related applications involving analysis of multi-party possibly privacy-sensitive data, and peer-topeer data stream mining in sensor and file-sharing networks. This talk will focus on peer-to-peer (P2P) distributed data stream mining and monitoring. It will first discuss the foundation of approximate and exact P2P algorithms for data analysis. Then it will present a class of P2P algorithms for eigen-analysis and clustering in details. The talk will end with a discussion on the future directions of research on P2P data mining.

Citations