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Graph based model for information retrieval. SLS method to extract subgraphs. SLS model versus Cosine model for information retrieval. SLS graph based model performance on the CACM collection. Graph has become increasingly important in modeling complicated structures and data such as chemical compounds, and social networks.
Recent advance of machine learning research has witnessed a number of models based on graphs, from which information retrieval study is also benefited since many of these models have been verified by different information retrieval tasks. To reduce the size of the index, we take into consideration the size of the query and the set of the frequent subgraphs. In other words, the subgraphs that will be used to create the index will have a size equal to the size of the query in order to optimize as much as possible the search space and the execution time. Our method is able to discover frequent subgraphs serving to establish the index and relevant documents for the Information Retrieval process’s output.
Check if you have access through your login credentials or your institution. Cluster ensemble has become an important extension to traditional clustering algorithms, yet the cluster ensemble problem is very challenging due to the inherent difficulty in resolving the label correspondence problem. Laplacian clustering approach to solve the cluster ensemble problem by exploiting both the attribute information embedded in the cluster labels and the pairwise relations among the objects. The optimal solution of the proposed approach requires computing the pseudo inverse of the normalized Laplacian matrix and the eigenvalue decomposition of a large matrix, which can be computationally burdensome for large scale document datasets. Experimental results with benchmark document datasets demonstrate that IKLCEA outperforms other cluster ensemble techniques on most cases. In addition, IKLCEA is computationally efficient and can be readily employed in large scale document applications.
Harbin Engineering University, Harbin, China, in 2007 and in 2010, respectively. His research interests include pattern recognition, machine learning and data mining. His current research focuses on document clustering. Now he is professor at University of Iowa, fellow of American Statistical Association, and fellow of Institute of Mathematical Statistics.
His field of interest includes time series analysis, chaos, semiparametric statistics, stochastic differential equation modeling, sampling-based inference, statistical ecology. His current research focuses on image classification and dimension reduction. Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2007. Her research interests include grid computing, pattern recognition, and computer application. Her current research focuses on intelligent information processing.
Nanjing University of Science and Technology, China, in 2006. In 2015, he received Ph. China University of Mining and Technology, China. His current research interests include machine learning and data mining.