Question Bank
... minimum risk based on their applications. 21 Write short notes on (a) data warehouse (b) multimedia databases (c) Time series data (a) Data warehouse: is a subject oriented, integrated, time variant and non volatile repository used for data mining purposes. (explain briefly) (b) Multimedia databases ...
... minimum risk based on their applications. 21 Write short notes on (a) data warehouse (b) multimedia databases (c) Time series data (a) Data warehouse: is a subject oriented, integrated, time variant and non volatile repository used for data mining purposes. (explain briefly) (b) Multimedia databases ...
Aalborg Universitet Inference in hybrid Bayesian networks
... unmanageably large. For instance, assume a simple case in which we deal with 10 discrete variables that have three states each. Specifying the joint distribution for those variables would be equivalent to defining a table with 310 − 1 = 59 048 probability values, i.e., the size of the distribution g ...
... unmanageably large. For instance, assume a simple case in which we deal with 10 discrete variables that have three states each. Specifying the joint distribution for those variables would be equivalent to defining a table with 310 − 1 = 59 048 probability values, i.e., the size of the distribution g ...
Statistical analysis of array data: Dimensionality reduction, clustering
... • K-means algorithm is an oline approximation of EM algorithm – maximizes the quadratic log-likelihood (minimizes quadratic distances of datapoints to their clusters centroids) • The EM algorithm is used to optimize the centers of each cluster (weighted variance is maximal) which means that we find ...
... • K-means algorithm is an oline approximation of EM algorithm – maximizes the quadratic log-likelihood (minimizes quadratic distances of datapoints to their clusters centroids) • The EM algorithm is used to optimize the centers of each cluster (weighted variance is maximal) which means that we find ...
An Overview of First-Order Model Counting
... Consider a randomly shuffled deck of 52 playing cards. Suppose that we are dealt the top card, and we want to answer: what is the probability that we get hearts? When the dealer reveals that the bottom card is black, how does our probability change? Basic statistics says it increases from 1/4 to 13/ ...
... Consider a randomly shuffled deck of 52 playing cards. Suppose that we are dealt the top card, and we want to answer: what is the probability that we get hearts? When the dealer reveals that the bottom card is black, how does our probability change? Basic statistics says it increases from 1/4 to 13/ ...
The Role of Discretization Parameters in Sequence Rule Evolution
... size is given in Sect. 2.3; informally, the resolution refers to the width of each (overlapping) discretized segment of the time series, while the alphabet size is simply the cardinality of the alphabet used in the resulting strings. As mentioned in the introduction, we will compare three methods of ...
... size is given in Sect. 2.3; informally, the resolution refers to the width of each (overlapping) discretized segment of the time series, while the alphabet size is simply the cardinality of the alphabet used in the resulting strings. As mentioned in the introduction, we will compare three methods of ...
Random Dot Product Graph Models for Social Networks
... existence of directed cycles, among others. Thus there is considerable interest in new models for complex networks that exhibit a power-law like degree sequence, small diameter, and clustering, and are different enough from the three main model classes to exhibit other properties of complex network ...
... existence of directed cycles, among others. Thus there is considerable interest in new models for complex networks that exhibit a power-law like degree sequence, small diameter, and clustering, and are different enough from the three main model classes to exhibit other properties of complex network ...
The Bayes classifier
... What happens when we get an article about hockey? To avoid this problem, it is common to instead use ...
... What happens when we get an article about hockey? To avoid this problem, it is common to instead use ...
Exponential Family Distributions
... N. Lawrence, M. Milo, M. Niranjan, P. Rashbass, and S. Soullier. Reducing the variability in microarray image processing by Bayesian inference. Technical report, Department of Computer Science, University of Sheffield, 2002. D. B. Lenat. CYC: A large-scale investment in knowledge infrastructure. Com ...
... N. Lawrence, M. Milo, M. Niranjan, P. Rashbass, and S. Soullier. Reducing the variability in microarray image processing by Bayesian inference. Technical report, Department of Computer Science, University of Sheffield, 2002. D. B. Lenat. CYC: A large-scale investment in knowledge infrastructure. Com ...
A Data-driven Approach for qu Prediction of Laboratory Soil
... values for all N examples. In a model for which such difference is close to zero a high accuracy is expected. In particular, three different metrics were calculated (Tinoco et al., 2011): Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Correlation (R2). Low values of MAE ...
... values for all N examples. In a model for which such difference is close to zero a high accuracy is expected. In particular, three different metrics were calculated (Tinoco et al., 2011): Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Correlation (R2). Low values of MAE ...
Clustering - Hong Kong University of Science and Technology
... For a given object in cluster Ck, if we guess its attribute values according to the probabilities of occurring, then the expected number of attribute values that we can correctly guess is ...
... For a given object in cluster Ck, if we guess its attribute values according to the probabilities of occurring, then the expected number of attribute values that we can correctly guess is ...
7. Markov chain Monte Carlo methods for sequence segmentation
... intensity descriptions from event sequence data. Proceedings of 7th ACM SIGKDD (KDD’2001) Conference on Knowledge Discovery and Data Mining, ...
... intensity descriptions from event sequence data. Proceedings of 7th ACM SIGKDD (KDD’2001) Conference on Knowledge Discovery and Data Mining, ...
Modeling Student Learning: Binary or Continuous Skill?
... binary latent variable (either learned or unlearned). Figure 1 illustrates the model; the illustration is done in a nonstandard way to stress the relation of the model to the model with continuous skill. The estimated skill is updated using a Bayes rule based on the observed answers; the prediction ...
... binary latent variable (either learned or unlearned). Figure 1 illustrates the model; the illustration is done in a nonstandard way to stress the relation of the model to the model with continuous skill. The estimated skill is updated using a Bayes rule based on the observed answers; the prediction ...
Initialization of Iterative Refinement Clustering Algorithms
... databases, the initial sample becomes negligible in size. If, for a data set D, a clustering algorithm requires Iter(D) iterations to cluster it, then time complexity is |D| * Iter(D). A small subsample S ⊆ D, where |S| << |D|, typically requires significantly fewer iteration to cluster. Empirically ...
... databases, the initial sample becomes negligible in size. If, for a data set D, a clustering algorithm requires Iter(D) iterations to cluster it, then time complexity is |D| * Iter(D). A small subsample S ⊆ D, where |S| << |D|, typically requires significantly fewer iteration to cluster. Empirically ...
Scaling EM Clustering to Large Databases Bradley, Fayyad, and
... addresses probabilistic clustering in which every data point belongs to all clusters, but with different probability. This generalizes to include realistic situations in which, say a customer of a web site, really belongs to two or more segments (e.g. sports enthusiast, high tech enthusiast, and cof ...
... addresses probabilistic clustering in which every data point belongs to all clusters, but with different probability. This generalizes to include realistic situations in which, say a customer of a web site, really belongs to two or more segments (e.g. sports enthusiast, high tech enthusiast, and cof ...
Replace Missing Values with EM algorithm based on GMM
... probability density function accurately quantify things, things will be decomposed into a lot of models based on the formation of Gaussian probability density function. Modeling process, we need some parameters of Gaussian mixture model, such as variance, mean, weights and other initialization param ...
... probability density function accurately quantify things, things will be decomposed into a lot of models based on the formation of Gaussian probability density function. Modeling process, we need some parameters of Gaussian mixture model, such as variance, mean, weights and other initialization param ...
An Abductive-Inductive Algorithm for Probabilistic
... In the next step, parameter learning is performed. This is done by performing XHAILs inductive task transformation on the theory generated from the structural learning step and then performing Peircebayes statistical abduction with the generated background and the use ground atoms as abducibles and ...
... In the next step, parameter learning is performed. This is done by performing XHAILs inductive task transformation on the theory generated from the structural learning step and then performing Peircebayes statistical abduction with the generated background and the use ground atoms as abducibles and ...