The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Principles of data mining

Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.

/pdf/incorporating-non-local-information-into-information-2upchk9bsy.pdf

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

Dynamic Bayesian Networks: Representation, Inference and Learning by Kevin Patrick Murphy Doctor of Philosophy in Computer Science University of California, Berkeley Professor Stuart Russell, Chair Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data. In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T ) time instead of O(T ), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T ) space instead of O(T ); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.

/pdf/dynamic-bayesian-networks-representation-inference-and-yy4fhfxdcj.pdf

Dynamic bayesian networks: representation, inference and learning

The Rise of the Creative Class

One role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web workload generation tool which mimics a set of real users accessing a server. The tool, called Surge (Scalable URL Reference Generator) generates references matching empirical measurements of 1) server file size distribution; 2) request size distribution; 3) relative file popularity; 4) embedded file references; 5) temporal locality of reference; and 6) idle periods of individual users. This paper reviews the essential elements required in the generation of a representative Web workload. It also addresses the technical challenges to satisfying this large set of simultaneous constraints on the properties of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner significantly different from other Web server benchmarks.

/pdf/generating-representative-web-workloads-for-network-and-2p17j04sw8.pdf

Generating representative Web workloads for network and server performance evaluation

As the authors note in the Preface, “Courses on Quality are generally taught in two different ways: ‘technique-oriented’ courses that emphasize statistical tools, and ‘management-oriented’ courses that stress the managerial aspects of quality. This book is intended as a main text for a managerial-oriented course on quality.” For the statistical approach, the authors refer you to their other book, Statistical Quality Control: Strategies and Tools for Continual Improvement (Ledolter and Burrill 1999). The book does an excellent job of achieving its objective. Through a large number of examples and case studies, it covers a wide range of managerial topics in 624 pages, six sections of 24 chapters. The sections are “Introduction to Quality,” “Process View of Quality,” “Management Issues in Achieving Quality,” “Stabilizing Quality,” “Improving Quality,” and “Conclusion— Optimism for the Future.” There is also an appendix with eight case studies. I was surprised that there were no mentions of the ASQ Certi ed Quality Manager certi cation since the book covers most, if not all, of the topics needed for that certi cation. It also covers not just manufacturing quality but “all aspects of quality: quality of goods, services and information.” There are more services and information examples than in most comparable books. There are many examples in the text and many exercises and additional readings at the end of each chapter. Since the book was copyrighted in 1999, there are even exercises speci cally asking for an Internet browser search. Although this part of the book is very up to date, many of the examples are not. Chapter 3 uses examples ranging from 1982–1993. Although they may be good examples, I wonder if the improvements noted have been maintained. The examples are also a good mix of Japanese and American companies— although with the current problems in Japan I would have like have preferred to see even fewer Japanese examples. Section IV on stabilizing quality has four chapters devoted to various statistical methods. Since this is not a statistically focused book, the topics are covered with a minimum of mathematics. However, they are covered in the depth appropriate for a management course. Interpretations are given, and shortcomings are noted. The book even covers new statistical topics like Cpm and Six-Sigma. In fact the entire book gives a very balanced description of various topics. Chapter 6 gives de nitions of quality by Deming, Juran, Feigenbaum, Crosby, Ishikawa, and ISO 8402. Each de nition includes a discussion. For example, the Deming discussion notes “some troublesome inconsistencies in Deming’s comments,” and Juran’s de nition includes a 1993 modi cation. It is good to see that all points of view are covered and each is critiqued. Overall this a very good, balanced, and comprehensive management book. Even the statistical quality control section is very well done. I recommend this as a college textbook or as a manager’s reference. It could even be used to supplement study for the ASQ Certi ed Quality Manager.

Probabilistic Networks and Expert Systems

SUMMARY We investigate robustness in the logistic regression model. Copas has studied two forms of robust estimator: a robust-resistant estimate of Pregibon and an estimate based on a misclassification model. He concluded that robust-resistant estimates are much more biased in small samples than the usual logistic estimate is and recommends a bias-corrected version of the misclassification estimate. We show that there are other versions of robust-resistant estimates which have bias often approximately the same as and sometimes even less than the logistic estimate; these estimates belong to the Mallows class. In addition, the corrected misclassification estimate is inconsistent at the logistic model; we develop a simple consistent modification. The modified estimate is a member of the Mallows class but, unlike most robust estimates, it has an interpretable tuning constant. The results are illustrated on data sets featuring different kinds of outliers.

On Robustness in the Logistic Regression Model

The survival model presented relates survival to tree size and vigor. Biological principles establish the general model form. The mathematical descriptor presented follows these biological principl...

A tree survival model with application to species of the Great Lakes region

A method and system is disclosed for enabling the accurate determination of price points (APRs), credit limits, and other discretionary levels for each cardholder that maximize Net Present Value (NPV) for the portfolio, given constraints on quantities such as risk of default. In accordance with one embodiment, the present invention uses a Markov Decision Process (MDP) methodology to generate a simplified transition matrix representative of the potential state transitions for account holders. This model applies account level historical information on purchases, payments, profitability and delinquency risk to make these decisions. In addition, a data structure is disclosed constructed to implement a transition matrix computationally in different sizes.

Method and system for enhancing credit line management, price management and other discretionary levels setting for financial accounts

A quantity, λ2 is defined as the discrepancy between an actual and an assumed statistical model. An estimator is developed for the case in which data are grouped. The estimator is based on Pearson'...

Estimating model discrepancy

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