Journal of software incremental updating algorithm
reliably manages a large amount of data in a multiuser environment so that many users can concurrently access the same data.
A database server also prevents unauthorized access and provides efficient solutions for failure recovery.
In recent years, the spectral clustering method has gained attentions because of its superior performance.
To the best of our knowledge, the existing spectral clustering algorithms cannot incrementally update the clustering results given a small change of the data set.
However, the capability of incrementally updating is essential to some applications such as websphere or blogsphere.
Unlike the traditional stream data, these applications require incremental algorithms to handle not only insertion/deletion of data points but also similarity changes between existing points.
The grid style of computing solves some common problems with enterprise IT: Compared with other models of computing, IT systems designed and implemented in the grid style deliver higher quality of service, lower cost, and greater flexibility.
Higher quality of service is achieved because there is no single point of failure, there is a robust security infrastructure, and management is centralized and policy-driven.
The major research achievements from his group include news video summarization, sports highlight detection, data clustering, and Smart Catch video surveillance that led to a successful spin-off. With this architecture, each new system can be rapidly provisioned from the pool of components.There is no need to provide extra hardware to support peak workloads, because capacity can be easily added or reallocated from the resource pools as needed. Because the physical and logical structures are separate, the physical storage of data can be managed without affecting access to logical storage structures.In this paper, we extend the standard spectral clustering to such evolving data, by introducing the to represent two kinds of dynamics in the same framework and by incrementally updating the eigen-system.Our incremental algorithm, initialized by a standard spectral clustering, continuously and efficiently updates the eigenvalue system and generates instant cluster labels, as the data set is evolving. Compared with recomputation of the solution by the standard spectral clustering, it achieves similar accuracy but with much lower computational cost.
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It can discover not only the stable blog communities but also the evolution of the individual multi-topic blogs. degrees in electronic engineering from the University of Tokyo in 1987, 1989, and 1992, respectively.