Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

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Scalable Molecular Dynamics with NAMD

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.

http://www-igm.univ-mlv.fr/~lombardy/ens/JavaTTT0708/fourmis.pdf

Ant colony system: a cooperative learning approach to the traveling salesman problem

Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called "affinity propagation," which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.

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Clustering by Passing Messages Between Data Points

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

When a two-dimensional (2D) traveling salesman problem (TSP) is presented on a computer screen, human subjects can produce near-optimal tours in linear time. In this study we tested human performance on a real and virtual floor, as well as in a threedimensional (3D) virtual space. Human performance on the real floor is as good as that on a computer screen. Performance on a virtual floor is very similar, while that in a 3D space is slightly but systematically worse. We modeled these results by a graph pyramid algorithm. The same algorithm can account for the results with 2D and 3D problems, which suggests that deterioration of performance in the 3D space can be attributed to geometrical relations between hierarchical clustering in a 3D space and coarse-to-fine production of a tour.

The traveling salesman problem

History (A. Hoffman and P. Wolfe). Motivation and Modeling (R. Garfinkel). Computational Complexity (D. Johnson and C. Papadimitriou). Well-Solved Special Cases (P. Gilmore, et al.). Performance Guarantees for Heuristics (D. Johnson and C. Papadimitriou). Probabilistic Analysis of Heuristics (R. Karp and J. Steele). Empirical Analysis of Heuristics (B. Golden and W. Stewart). Polyhedral Theory (M. Grotschel and M. Padberg). Polyhedral Algorithms (M. Padberg and M. Grotschel). Branch and Bound Methods (E. Balas and P. Toth). Hamiltonian Cycles (V. Chvatal). Vehicle Routing (N. Christofides). Bibliography.

The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization

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Sequencing and scheduling: algorithms and complexity

Sequencing and scheduling as a research area is motivated by questions that arise in production planning, in computer control, and generally in all situations in which scarce resources have to be allocated to activities over time. In this survey, we concentrate on the area of deterministic machine scheduling. We review complexity results and optimization and approximation algorithms for problems involving a single machine, parallel machines, open shops, flow shops and job shops. We also pay attention to two extensions of this area: resource-constrained project scheduling and stochastic machine scheduling.

Sequencing and scheduling : algorithms and complexity

In this paper we present a 2-approximation algorithm for the k-center problem with triangle inequality. This result is “best possible” since for any δ < 2 the existence of δ-approximation algorithm would imply that P = NP. It should be noted that no δ-approximation algorithm, for any constant δ, has been reported to date. Linear programming duality theory provides interesting insight to the problem and enables us to derive, in O|E| log |E| time, a solution with value no more than twice the k-center optimal value.

A by-product of the analysis is an O|E| algorithm that identifies a dominating set in G2, the square of a graph G, the size of which is no larger than the size of the minimum dominating set in the graph G. The key combinatorial object used is called a strong stable set, and we prove the NP-completeness of the corresponding decision problem.

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David B. Shmoys

Papers

The traveling salesman problem

The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization

Sequencing and scheduling: algorithms and complexity

Sequencing and scheduling : algorithms and complexity

A Best Possible Heuristic for the k-Center Problem