Yixian Yang

Web user clustering and Web prefetching using Random Indexing with weight functions

Users of a Web site usually perform their interest-oriented actions by clicking or visiting Web pages, which are traced in access log files. Clustering Web user access patterns may capture common user interests to a Web site, and in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. The conventional Web usage mining techniques for clustering Web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users’ navigational behaviour. In this paper, we propose an approach based on a vector space model, called Random Indexing, to discover such intrinsic characteristics of Web users’ activities. The underlying factors are then utilised for clustering individual user navigational patterns and creating common user profiles. The clustering results will be used to predict and prefetch Web requests for grouped users. We demonstrate the usability and superiority of the proposed Web user clustering approach through experiments on a real Web log file. The clustering and prefetching tasks are evaluated by comparison with previous studies demonstrating better clustering performance and higher prefetching accuracy.

A random indexing approach for web user clustering and web prefetching

In this paper we present a novel technique to capture Web users behaviour based on their interest-oriented actions. In our approach we utilise the vector space model Random Indexing to identify the latent factors or hidden relationships among Web users navigational behaviour. Random Indexing is an incremental vector space technique that allows for continuous Web usage mining. User requests are modelled by Random Indexing for individual users navigational pattern clustering and common user profile creation. Clustering Web users access patterns may capture common user interests and, in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. We present results from the Web user clustering approach through experiments on a real Web log file with promising results. We also apply our data to a prefetching task and compare that with previous approaches. The results show that Random Indexing provides more accurate prefetchings.

Parameter identification and projective synchronization between different chaotic systems.

In this paper, adaptive generalized projective synchronization and parameter identification in different chaotic systems are carefully revisited. We use a concrete counterexample to demonstrate that the result in a previous work [R. Li, W. Xu, and S. Li, Phys. Lett. A 367, 199 (2007)] is imperfect, where a scheme of generalized projective synchronization is proposed for parameter identification with some drawbacks on ignoring the conditions which ensure the parameter convergence. We further discuss the two conditions of parameter convergence, which are linear independence and persistent excitation. A special relationship between them is addressed to estimate unknown model parameters effectively.

Exponentially asymptotical synchronization in uncertain complex dynamical networks with time delay

Over the past decade, complex dynamical network synchronization has attracted more and more attention and important developments have been made. In this paper, we explore the scheme of globally exponentially asymptotical synchronization in complex dynamical networks with time delay. Based on Lyapunov stability theory and through defining the error function between adjacent nodes, four novel adaptive controllers are designed under four situations where the Lipschitz constants of the state function in nodes are known or unknown and the network structure is certain or uncertain, respectively. These controllers could not only globally asymptotically synchronize all nodes in networks, but also ensure that the error functions do not exceed the pre-scheduled exponential function. Finally, simulations of the synchronization among the chaotic system in the small-world and scale-free network structures are presented, which prove the effectiveness and feasibility of our controllers.

Parameter estimation of nonlinear dynamical systems based on integrator theory

In this paper, a novel unknown parameter identifier of nonlinear dynamical systems is designed through the integrator theory, and the corresponding sufficient conditions for the existence of unknown parameter identifiers are presented. In order to illustrate the effectiveness of the proposed method, simulation results are given. The effects of system noise and measurement noise for the proposed method are discussed in detail. The comparative analysis between the proposed method based on integrator theory and the approach based on adaptive synchronization is also given.

Parameter Identification and Synchronization of Dynamical System by Introducing an Auxiliary Subsystem

We propose a novel approach of parameter identification using the adaptive synchronized observer by introducing an auxiliary subsystem, and some sufficient conditions are given to guarantee the convergence of synchronization and parameter identification. We also demonstrate the mean convergence of synchronization and parameters identification under the influence of noise. Furthermore, in order to suppress the influence of noise, we complement a filter in the output. Numerical simulations on Lorenz and Chen systems are presented to demonstrate the effectiveness of the proposed approach.