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In this paper we propose a web log mining-based network user behavior analysis scheme, which plays an important role in network structure optimization and website server configuration. Based on clustering and regression model, we studied the network user's visit model in a university by analyzing a large amount of web log data which is collected from the university campus network. The data analyzing...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
C-regression models are known as very useful tools in many fields. Since now, many trials to construct c-regression models for data with uncertainty in independent and dependent variables have been done. However, there are few c-regression models for data with uncertainty in independent variables in comparison with dependent variables now. The reason is as follows. The models are constructed using...
This paper discusses the two important phases, which are data preprocessing and clustering analysis, in Web transactions clustering analysis, in order to gain an easily interpreted clustering result, we introduce the "Concept URL" in the data preprocessing phase; In the clustering analysis phase, A model of artificial ant is set up. Based on this model, we implement an ant-colony clustering...
Clustering of real-world data is often ill-posed. Because of noise and intrinsic ambiguity in data, optimization models attempting to maximize a fitness function can be misled by the assumption of uniqueness of the solution. In this work we present a methodology including classic and novel techniques to approach clustering in a systematic way, with two application examples to biological data sets...
In this paper, a distributed Expectation Maximization (EM) algorithm is proposed for estimating parameters of a Gaussian mixture model in a peer-to-peer network. This algorithm is used for density estimation and clustering of data distributed over nodes of a network. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local...
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent...
Fuzzy c-regression models (FCRM) performs switching regression based on a Fuzzy c-means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal...
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