Support vector data description shell algorithm

Abstract

The invention relates to a support vector data description shell algorithm. In response to problems and characteristics of data analysis model conformation and integration in distributed environment and bases on support vector data description (SVDD) algorithm, surrounding contour of the data is descried while parameters of the system are controlled, potential support vectors outside the contour are reserved, data which has no effect on future model conformation and integration is deleted, a hyper-spherical shell of potential support vector with certain thickness which describes data characteristics is formed, and the potential support vector quantity and model precision are balanced, thereby reaching the goal of expressing the model precisely by as little data as possible and reducing future model confirmation and integrating venture as a whole. The algorithm is applicable to models which are conformed and integrated by nodes in the distributed environment and to models which are found by analyses of different nodes.

Description

Technical field [0001] The invention relates to a data mining technology, in particular to a support vector data description shell algorithm. It is data flow analysis and mining technology and support vector machine technology in a distributed environment, focusing on solving the problem of model integration in a distributed environment. Background technique [0002] At present, the traditional data analysis method is based on batch data collection, stored in a certain medium, and then analyzed. With the rapid development of the information society, massive amounts of data are no longer and cannot be completely stored, and are analyzed by reading them more than once. This kind of stream composed of a group of data arriving in chronological order, at an uncertain rate, continuous, massive, and potentially infinite is called a data stream. Multiple data streams from different data sources form multiple data streams. In the computer network environment, multiple data streams are inp...

AI Technical Summary

Problems solved by technology

For this kind of data, especially distributed data streams, the method of batch storage and then analysis is no longer competent. Its main disadvantages are: the storage space cannot be estimated; it cannot be analyzed and processed in real time

On the other hand, in a distributed environment, it is unrealistic for each data source to transfer all the data in the window including the model to other nodes

Moreover, in some application backgrounds (such as banks), for the sake of data security, it is not possible to transmit a large amount of local data on the network

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