The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

This paper reviews the mathematical basis of maximum likelihood The likelihood function for macromolecular structures is extended to include prior phase information and experimental standard uncertainties The assumption that different parts of a structure might have different errors is considered A method for estimating σA using `free' reflections is described and its effects analysed The derived equations have been implemented in the program REFMAC This has been tested on several proteins at different stages of refinement (bacterial α-amylase, cytochrome c′, cross-linked insulin and oligopeptide binding protein) The results derived using the maximum-likelihood residual are consistently better than those obtained from least-squares refinement

Refinement of macromolecular structures by the maximum-likelihood method.

Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?

We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ~N2 for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the GNU General Public License v2.

https://iopscience.iop.org/article/10.1086/670067/pdf

emcee: The MCMC Hammer

Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algorithms for NMF are analyzed. They differ only slightly in the multiplicative factor used in the update rules. One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence. The monotonic convergence of both algorithms can be proven using an auxiliary function analogous to that used for proving convergence of the Expectation-Maximization algorithm. The algorithms can also be interpreted as diagonally rescaled gradient descent, where the rescaling factor is optimally chosen to ensure convergence.

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Algorithms for Non-negative Matrix Factorization

Numerical Recipes, The Art of Scientific Computing

From the Publisher:
Physics is a discipline which lends itself especially well to visualization This text teaches physics through computer simulation using TrueBasic--a friendly, accessible, non-commercialized or packaged language The emphasis is on physics instruction through computer simulation as opposed to teaching programming or numerical analysis

An Introduction to Computer Simulation Methods: Applications to Physical Systems

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An introduction to computer simulation methods

This short book gives a solid introduction to the field. After a review of direct methods for the solution of linear systems, following chapters introd and analyze the more commonly used finite difference methods for solving a variety of problems. Annotation copyright Book News, Inc. Portland, Or.

The Numerical Solution of Ordinary and Partial Differential Equations

/pdf/an-introduction-to-computer-simulation-methods-applications-2pv3egj4qq.pdf

Harvey Gould

Papers

Numerical Recipes, The Art of Scientific Computing

An Introduction to Computer Simulation Methods: Applications to Physical Systems

An introduction to computer simulation methods

The Numerical Solution of Ordinary and Partial Differential Equations

An Introduction to Computer Simulation Methods: Applications to Physical Systems (3rd Edition)