Distributed and Stream Data Mining Algorithms for
... In several interesting application frameworks, such as wireless network analysis and fraud detection, data are naturally distributed among several entities and/or evolve continuously. In all of the above-indicated data mining tasks, dealing with either of these peculiarities provides additional chal ...
... In several interesting application frameworks, such as wireless network analysis and fraud detection, data are naturally distributed among several entities and/or evolve continuously. In all of the above-indicated data mining tasks, dealing with either of these peculiarities provides additional chal ...
Document Clustering Using Locality Preserving Indexing
... graph partitioning perspective, the spectral clustering tries to find the best cut of the graph so that the predefined criterion function can be optimized. Many criterion functions, such as the ratio cut [4], average association [23], normalized cut [23], and min-max cut [8] have been proposed along ...
... graph partitioning perspective, the spectral clustering tries to find the best cut of the graph so that the predefined criterion function can be optimized. Many criterion functions, such as the ratio cut [4], average association [23], normalized cut [23], and min-max cut [8] have been proposed along ...
Abnormal Pattern Recognition in Spatial Data
... from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden bu ...
... from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden bu ...
CORDS: Automatic Discovery of Correlations and Soft Functional
... also cause query optimizers—which usually assume that columns are statistically independent—to underestimate the selectivities of conjunctive predicates by orders of magnitude. We introduce cords, an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies ...
... also cause query optimizers—which usually assume that columns are statistically independent—to underestimate the selectivities of conjunctive predicates by orders of magnitude. We introduce cords, an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies ...
Here - Advanced Computing Group home page
... determines feature relevance by evaluating feature’s correlation with the class, and without labels, unsupervised feature selection exploits data variance and separability to evaluate feature relevance [11, 21]. Semi-supervised feature selection algorithms [68, 58] can use both labeled and unlabele ...
... determines feature relevance by evaluating feature’s correlation with the class, and without labels, unsupervised feature selection exploits data variance and separability to evaluate feature relevance [11, 21]. Semi-supervised feature selection algorithms [68, 58] can use both labeled and unlabele ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.