CiteULike: AbnerCYH's kdd

A theory of learning with similarity functions

Maria-Florina Balcan — 2008-05-18T15:05:42-00:00

Machine Learning

Abstract Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a possibly very high dimensional space, and describes a kernel function as being good for a given learning problem if data is separable by a large margin in that implicit space. However, while quite elegant, this theory does not necessarily correspond to the intuition of a good kernel as a good measure of similarity, and the underlying margin in the implicit space usually is not apparent in “natural” representations of the data. Therefore, it may be difficult for a domain expert to use the theory to help design an appropriate kernel for the learning task at hand. Moreover, the requirement of positive semi-definiteness may rule out the most natural pairwise similarity functions for the given problem domain. In this work we develop an alternative, more general theory of learning with similarity functions (i.e., sufficient conditions for a similarity function to allow one to learn well) that does not require reference to implicit spaces, and does not require the function to be positive semi-definite (or even symmetric). Instead, our theory talks in terms of more direct properties of how the function behaves as a similarity measure. Our results also generalize the standard theory in the sense that any good kernel function under the usual definition can be shown to also be a good similarity function under our definition (though with some loss in the parameters). In this way, we provide the first steps towards a theory of kernels and more general similarity functions that describes the effectiveness of a given function in terms of natural similarity-based properties.

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,

Yoav Freund — 2008-05-15T01:39:48-00:00

Journal of Computer and System Sciences, Vol. 55, No. 1. (August 1997), pp. 119-139.

In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update Littlestone-Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games, and prediction of points in n. In the second part of the paper we apply the multiplicative weight-update technique to derive a new boosting algorithm. This boosting algorithm does not require any prior knowledge about the performance of the weak learning algorithm. We also study generalizations of the new boosting algorithm to the problem of learning functions whose range, rather than being binary, is an arbitrary finite set or a bounded segment of the real line.

Complex networks: Lies, damned lies and statistics

Nunes — 2008-03-09T16:48:04-00:00

Nat Phys, Vol. 2, No. 2. (February 2006), pp. 75-76.

Singular Value Decomposition and Principal Component Analysis

Michael Wall — 2005-10-17T02:36:40-00:00

(3 Mar 2003)

This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.

Constructing boosting algorithms from SVMs: an application to one-class classification

G Ratsch — 2008-05-10T07:08:19-00:00

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 24, No. 9. (September 2002), pp. 1184-1199.

We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm—one-class leveraging—starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

The support vector machine under test

David Meyer — 2008-05-09T19:40:26-00:00

Neurocomputing, Vol. 55, No. 1-2. (September 2003), pp. 169-186.

Support vector machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methods--all accessible through the software --by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.

The boosting approach to machine learning: An overview

R Schapire — 2005-11-30T08:57:07-00:00

(2001)

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost's training error and generalization error; boosting's connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge...

Protein Structure from Contact Maps: A Case-Based Reasoning Approach

Janice Glasgow — 2006-02-25T13:18:23-00:00

Information Systems Frontiers, Vol. 8, No. 1. (February 2006), pp. 29-36.

An introduction to ROC analysis

Tom Fawcett — 2006-08-29T01:24:20-00:00

Pattern Recognition Letters, Vol. 27, No. 8. (June 2006), pp. 861-874.

Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

Using AUC and accuracy in evaluating learning algorithms

Jin Huang — 2008-05-09T12:01:38-00:00

Knowledge and Data Engineering, IEEE Transactions on, Vol. 17, No. 3. (2005), pp. 299-310.

The area under the ROC (receiver operating characteristics) curve, or simply AUC, has been traditionally used in medical diagnosis since the 1970s. It has recently been proposed as an alternative single-number measure for evaluating the predictive ability of learning algorithms. However, no formal arguments were given as to why AUC should be preferred over accuracy. We establish formal criteria for comparing two different measures for learning algorithms and we show theoretically and empirically that AUC is a better measure (defined precisely) than accuracy. We then reevaluate well-established claims in machine learning based on accuracy using AUC and obtain interesting and surprising new results. For example, it has been well-established and accepted that Naive Bayes and decision trees are very similar in predictive accuracy. We show, however, that Naive Bayes is significantly better than decision trees in AUC. The conclusions drawn in this paper may make a significant impact on machine learning and data mining applications.

The use of receiver operating characteristic curves in biomedical informatics

Thomas Lasko — 2006-01-14T16:08:59-00:00

Journal of Biomedical Informatics, Vol. 38, No. 5. (October 2005), pp. 404-415.

Receiver operating characteristic (ROC) curves are frequently used in biomedical informatics research to evaluate classification and prediction models for decision support, diagnosis, and prognosis. ROC analysis investigates the accuracy of a model's ability to separate positive from negative cases (such as predicting the presence or absence of disease), and the results are independent of the prevalence of positive cases in the study population. It is especially useful in evaluating predictive models or other tests that produce output values over a continuous range, since it captures the trade-off between sensitivity and specificity over that range. There are many ways to conduct an ROC analysis. The best approach depends on the experiment; an inappropriate approach can easily lead to incorrect conclusions. In this article, we review the basic concepts of ROC analysis, illustrate their use with sample calculations, make recommendations drawn from the literature, and list readily available software.

Finding frequent patterns in a large sparse graph

M Kuramochi — 2006-11-18T21:03:10-00:00

(2004)

This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edgedisjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edge-disjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation ...

Mining the Web: Analysis of Hypertext and Semi Structured Data

Soumen Chakrabarti — 2005-03-02T15:59:19-00:00

(15 August 2002)

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort. * A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining. * Details the special challenges associated with analyzing unstructured and semi-structured data. * Looks at how classical Information Retrieval techniques have been modified for use with Web data. * Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning. * Analyzes current applications for resource discovery and social network analysis. * An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul>

Statistical Learning Theory

Vladimir Vapnik — 2005-02-28T20:55:16-00:00

(16 September 1998)

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

An Introduction to Computational Learning Theory

Michael Kearns — 2006-04-18T12:41:53-00:00

(15 August 1994)

Pattern Recognition with Fuzzy Objective Function Algorithms

James Bezdek — 2008-04-02T05:09:59-00:00

(1981)

A theory of the learnable

LG Valiant — 2006-01-16T21:03:03-00:00

Commun. ACM, Vol. 27, No. 11. (November 1984), pp. 1134-1142.

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

Bernhard Schlkopf — 2007-12-08T16:45:46-00:00

(15 December 2001)

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Nello Cristianini — 2005-03-05T09:49:19-00:00

(23 March 2000)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

A Tutorial on Support Vector Machines for Pattern Recognition

Christopher Burges — 2006-02-06T08:07:22-00:00

Data Min. Knowl. Discov., Vol. 2, No. 2. (June 1998), pp. 121-167.

Graph algorithms for biological systems analysis

Bonnie Berger — 2008-03-19T08:13:33-00:00

(2008), pp. 142-151.

Aspects of discrete mathematics and probability in the theory of machine learning

Martin Anthony — 2008-02-27T02:41:05-00:00

Discrete Applied Mathematics, Vol. 156, No. 6. (15 March 2008), pp. 883-902.

This paper discusses the applications of certain combinatorial and probabilistic techniques to the analysis of machine learning. Probabilistic models of learning initially addressed binary classification (or pattern classification). Subsequently, analysis was extended to regression problems, and to classification problems in which the classification is achieved by using real-valued functions (where the concept of a large margin has proven useful). Another development, important in obtaining more applicable models, has been the derivation of data-dependent bounds. Here, we discuss some of the key probabilistic and combinatorial techniques and results, focusing on those of most relevance to researchers in discrete applied mathematics.

Bidirectional expansion for keyword search on graph databases

Varun Kacholia — 2008-02-13T10:36:48-00:00

(2005), pp. 505-516.

Minimization of decision trees is hard to approximate

Detlef Sieling — 2008-01-22T14:43:43-00:00

Journal of Computer and System Sciences, Vol. 74, No. 3. (May 2008), pp. 394-403.

Decision trees are representations of discrete functions with widespread applications in, e.g., complexity theory and data mining and exploration. In these areas it is important to obtain decision trees of small size. The minimization problem for decision trees is known to be NP-hard. In this paper the problem is shown to be even hard to approximate up to any constant factor under the assumption P[not equal to]NP.

Symbolic dynamic analysis of complex systems for anomaly detection

Asok Ray — 2007-12-27T17:23:56-00:00

Signal Process., Vol. 84, No. 7. (July 2004), pp. 1115-1130.

Pattern identification in dynamical systems via symbolic time series analysis

Venkatesh Rajagopalan — 2007-12-27T17:23:53-00:00

Pattern Recogn., Vol. 40, No. 11. (November 2007), pp. 2897-2907.

Symbolic time series analysis for anomaly detection: a comparative evaluation

Shin Chin — 2007-03-12T13:21:05-00:00

Signal Process., Vol. 85, No. 9. (September 2005), pp. 1859-1868.

An n log n Algorithm for Online BDD Refinement

Nils Klarlund — 2007-12-17T17:38:44-00:00

Journal of Algorithms, Vol. 32, No. 2. (August 1999), pp. 133-154.

Binary decision diagrams are in widespread use in verification systems for the canonical representation of finite functions. Here we consider multivalued BDDs, which represent functions of the form [phi]: [nu] --> , where is a finite set of leaves. We study a rather natural online BDD refinement problem: a partition of the leaves of several shared BDDs is gradually refined, and the equivalence of the BDDs under the current partition must be maintained in a discriminator table. We show that it can be solved in O(n log n) time if n bounds both the size of the BDDs and the total size of update operations. Our algorithm is based on an understanding of BDDs as the fixed points of an operator that in each step splits and gathers nodes. We apply our algorithm to show that automata BDD-represented transition functions can be minimized in time O(n [middle dot] log n), where n is the total number of BDD nodes representing the automaton. This result is not an instance of Hopcroft's classical minimization algorithm, which breaks down for BDD-represented automata because of the BDD path compression property.

Getting Started in Probabilistic Graphical Models

Edoardo Airoldi — 2007-12-07T19:30:55-00:00

PLoS Computational Biology, Vol. 3, No. 12. (1 December 2007), e252.

Graphgrep: A fast and universal method for querying graphs

R Giugno — 2007-12-06T03:04:37-00:00

(2002)

GraphGrep is an application-independent method for querying graphs, finding all the occurrences of a subgraph in a database of graphs. The interface to GraphGrep is a regular expression graph query language Glide that combines features from XPath and Smart. Glide incorporates both single node and variable-length wildcards. Our algorithm uses hash-based fingerprinting to represent the graphs in an abstract form and to filter the database. GraphGrep has been tested on databases of size up to...

Optimization and scale-freeness for complex networks

Petter Minnhagen — 2007-11-10T15:55:38-00:00

Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 17, No. 2. (2007)

Complex networks are mapped to a model of boxes and balls where the balls are distinguishable. It is shown that the scale-free size distribution of boxes maximizes the information associated with the boxes provided configurations including boxes containing a finite fraction of the total amount of balls are excluded. It is conjectured that for a connected network with only links between different nodes, the nodes with a finite fraction of links are effectively suppressed. It is hence suggested that for such networks the scale-free node-size distribution maximizes the information encoded on the nodes. The noise associated with the size distributions is also obtained from a maximum entropy principle. Finally, explicit predictions from our least bias approach are found to be borne out by metabolic networks.

Algebraic decision trees and Euler characteristics

Andrew Yao — 2005-09-28T15:25:48-00:00

Theor. Comput. Sci., Vol. 141, No. 1-2. (1995), pp. 133-150.

For any set Image, let χ(S) denote its Euler characteristic. In this paper, we show that any algebraic computation tree or fixed-degree algebraic decision tree must have height Ω(log¦χ(S)¦ − cn) for deciding the membership question of a compact semi-algebraic set S. This extends a result in Björner et al. (1992), where it was shown that any linear decision tree for deciding the membership question of a closed polyhedron S must have height greater than or equal to log3¦χ(S)¦.

Studying Recommendation Algorithms by Graph Analysis

Batul Mirza — 2007-11-09T09:46:16-00:00

Journal of Intelligent Information Systems, Vol. 20, No. 2. (1 March 2003), pp. 131-160.

We present a novel framework for studying recommendation algorithms in terms of the 'jumps' that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset and allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm 'jump,' what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the 'hammock' using movie recommender datasets.

The Complexity of Cooperation

Robert Axelrod — 2006-04-29T16:20:44-00:00

(18 August 1997)

Robert Axelrod is widely known for his groundbreaking work in game theory and complexity theory. He is a leader in applying computer modeling to social science problems. His book The Evolution of Cooperation has been hailed as a seminal contribution and has been translated into eight languages since its initial publication. The Complexity of Cooperation is a sequel to that landmark book. It collects seven essays, originally published in a broad range of journals, and adds an extensive new introduction to the collection, along with new prefaces to each essay and a useful new appendix of additional resources. Written in Axelrod's acclaimed, accessible style, this collection serves as an introductory text on complexity theory and computer modeling in the social sciences and as an overview of the current state of the art in the field.The articles move beyond the basic paradigm of the Prisoner's Dilemma to study a rich set of issues, including how to cope with errors in perception or implementation, how norms emerge, and how new political actors and regions of shared culture can develop. They use the shared methodology of agent-based modeling, a powerful technique that specifies the rules of interaction between individuals and uses computer simulation to discover emergent properties of the social system. The Complexity of Cooperation is essential reading for all social scientists who are interested in issues of cooperation and complexity Robert Axelrod is widely known for his groundbreaking work in game theory and complexity theory. He is a leader in applying computer modeling to social science problems. His book The Evolution of Cooperation has been hailed as a seminal contribution and has been translated into eight languages since its initial publication. The Complexity of Cooperation is a sequel to that landmark book. It collects seven essays, originally published in a broad range of journals, and adds an extensive new introduction to the collection, along with new prefaces to each essay and a useful new appendix of additional resources. Written in Axelrod's acclaimed, accessible style, this collection serves as an introductory text on complexity theory and computer modeling in the social sciences and as an overview of the current state of the art in the field. The articles move beyond the basic paradigm of the Prisoner's Dilemma to study a rich set of issues, including how to cope with errors in perception or implementation, how norms emerge, and how new political actors and regions of shared culture can develop. They use the shared methodology of agent-based modeling, a powerful technique that specifies the rules of interaction between individuals and uses computer simulation to discover emergent properties of the social system. The Complexity of Cooperation is essential reading for all social scientists who are interested in issues of cooperation and complexity

Graph evolution: Densification and shrinking diameters

Jure Leskovec — 2007-05-10T07:09:04-00:00

ACM Trans. Knowl. Discov. Data, Vol. 1, No. 1. (March 2007)

Combining Pattern Classifiers: Methods and Algorithms

Ludmila Kuncheva — 2006-09-02T17:12:18-00:00

(01 July 2004)

Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading. Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.

Introduction to Data Mining

Pang-Ning Tan — 2006-04-21T21:37:56-00:00

(02 May 2005)

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Jiawei Han — 2005-07-29T09:22:35-00:00

(06 September 2000)

Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases. Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as 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. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success. Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms. Classroom Features Available Online: - instructor's manual - course slides (in PowerPoint) - course supplementary readings - sample assignments and course projects * Offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. * Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn. * Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects. * Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis. * 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.

Correlation search in graph databases

Yiping Ke — 2007-08-21T13:19:17-00:00

(2007), pp. 390-399.

Graphical Models: Methods for Data Analysis and Mining

Christian Borgelt — 2006-01-31T18:13:15-00:00

(15 March 2002)

The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data. One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example. Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data. Each concept is carefully explained and illustrated by examples. Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions. Features applications of learning graphical models from data, and problems for further research. Includes a comprehensive bibliography. Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject.

Data Mining: Introductory and Advanced Topics

Margaret Dunham — 2007-06-04T10:22:17-00:00

(22 August 2002)

Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)

Dorian Pyle — 2007-08-09T14:42:26-00:00

(15 March 1999)

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results. On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment. * Offers in-depth coverage of an essential but largely ignored subject. * Goes far beyond theory, leading you-step by step-through the author's own data preparation techniques. * Provides practical illustrations of the author's methodology using realistic sample data sets. * Includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required. * Explains how to identify and correct data problems that may be present in your application. * Prepares miners, helping them head into preparation with a better understanding of data sets and their limitations.

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Ian Witten — 2005-10-04T14:35:45-00:00

(08 June 2005)

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. + Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award. + Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods; + Performance improvement techniques that work by transforming the input or output; + Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface.

Advances in Knowledge Discovery and Data Mining

Usama Fayyad — 2007-08-09T14:42:27-00:00

(01 February 1996)

Advances in Knowledge Discovery and Data Mining brings together the latest research -- in statistics, databases, machine learning, and artificial intelligence -- that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. Topics covered include fundamental issues, classification and clustering, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, and application case studies. The contributors include leading researchers and practitioners from academia, government laboratories, and private industry. The last decade has seen an explosive growth in the generation and collection of data. Advances in data collection, widespread use of bar codes for most commercial products, and the computerization of many business and government transactions have flooded us with data and generated an urgent need for new techniques and tools that can intelligently and automatically assist in transforming this data into useful knowledge. This book is a timely and comprehensive overview of the new generation of techniques and tools for knowledge discovery in data. Distributed for AAAI Press

Out-of-core coherent closed quasi-clique mining from large dense graph databases

Zhiping Zeng — 2007-06-21T03:53:04-00:00

ACM Trans. Database Syst., Vol. 32, No. 2. (June 2007)

Mining the space of graph properties

Glen Jeh — 2006-05-12T21:36:34-00:00

(2004), pp. 187-196.

Graph mining: Laws, generators, and algorithms

Deepayan Chakrabarti — 2006-07-21T13:00:13-00:00

ACM Comput. Surv., Vol. 38, No. 1. (2006)

The Elements of Statistical Learning

T Hastie — 2005-04-15T14:57:05-00:00

(09 August 2001)

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes theimprtant ideas in these areas ina common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a vluable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

ANF: a fast and scalable tool for data mining in massive graphs

Christopher Palmer — 2006-02-09T09:53:42-00:00

(2002), pp. 81-90.