A Survey on Distributed Data Mining and Its Trends

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. The Data Mining technology normally adopts data integration method to generate Data warehouse, on which to gather all data into a central site, and then run an algorithm against that data to extract the useful Module Prediction and knowledge evaluation. However, a single data-mining technique has not been proven appropriate for every domain and data set. Data mining techniques involving in such complex environment must encounter great dynamics due to changes in the system can affect the overall performance of the system. Distributed data mining is originated from the need of mining over decentralized data sources. The field of Distributed Data Mining (DDM) deals with these challenges in analyzing distributed data and offers many algorithmic solutions to perform different data analysis and mining operations in a fundamentally distributed manner that pays careful attention to the resource constraints. This paper is a survey concerned with Distributed Data Mining algorithms, methods and trends in order to discover knowledge from distributed data in an effective and efficient way.