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Full-Text Articles in Physical Sciences and Mathematics
Exploratory Factor Analysis Of Graphical Features For Link Prediction In Social Networks, Lale Madahali, Lotfi Najjar, Margeret Hall
Exploratory Factor Analysis Of Graphical Features For Link Prediction In Social Networks, Lale Madahali, Lotfi Najjar, Margeret Hall
Interdisciplinary Informatics Faculty Proceedings & Presentations
Social Networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link prediction problem: feature-based models, Bayesian probabilistic models, probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exists three groups of features, neighborhood features, path-based features, …
A Pseudo Nearest-Neighbor Approach For Missing Data Recovery On Gaussian Random Data Sets, Xiaolu Huang, Qiuming Zhu
A Pseudo Nearest-Neighbor Approach For Missing Data Recovery On Gaussian Random Data Sets, Xiaolu Huang, Qiuming Zhu
Computer Science Faculty Publications
Missing data handling is an important preparation step for most data discrimination or mining tasks. Inappropriate treatment of missing data may cause large errors or false results. In this paper, we study the effect of a missing data recovery method, namely the pseudo- nearest neighbor substitution approach, on Gaussian distributed data sets that represent typical cases in data discrimination and data mining applications. The error rate of the proposed recovery method is evaluated by comparing the clustering results of the recovered data sets to the clustering results obtained on the originally complete data sets. The results are also compared with …
An Iterative Initial-Points Refinement Algorithm For Categorical Data Clustering, Ying Sun, Qiuming Zhu, Zhengxin Chen
An Iterative Initial-Points Refinement Algorithm For Categorical Data Clustering, Ying Sun, Qiuming Zhu, Zhengxin Chen
Computer Science Faculty Publications
The original k-means clustering algorithm is designed to work primarily on numeric data sets. This prohibits the algorithm from being directly applied to categorical data clustering in many data mining applications. The k-modes algorithm [Z. Huang, Clustering large data sets with mixed numeric and categorical value, in: Proceedings of the First Pacific Asia Knowledge Discovery and Data Mining Conference. World Scientific, Singapore, 1997, pp. 21–34] extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, as is …