Data Mining by Mehmed Kantardzic (inspirational novels TXT) 📗

interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning, and databases, the literature is scattered among many different sources. In this article, we present an overview of the techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams. We also describe some recent results regarding statistical analysis of pattern discovery methods.

Mitsa T., Temporal Data Mining, Chapmann & Hall/CRC Press, Boca Raton, FL, 2010.

From basic data-mining concepts to state-of-the-art advances, Temporal Data Mining covers the theory of this subject as well as its application in a variety of fields. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. The book also explores the use of temporal data mining in medicine and biomedical informatics, business and industrial applications, Web-usage mining, and spatiotemporal data mining. Along with various state-of-the-art algorithms, each chapter includes detailed references and short descriptions of relevant algorithms and techniques described in other references. In the appendices, the author explains how data mining fits the overall goal of an organization and how these data can be interpreted for the purposes of characterizing a population. She also provides programs written in the Java language that implement some of the algorithms presented in the first chapter.

Pearl, J., Causality: Models, Reasoning and Inference, 2nd edition, Cambridge University Press, Cambridge, UK, 2009.

This book fulfills a long-standing need for a rigorous yet accessible treatise on the mathematics of causal inference. Judea Pearl has done a masterful job of describing the most important approaches and displaying their underlying logical unity. The book deserves to be read by all scientists who use nonexperimental data to study causation, and would serve well as a graduate or advanced undergraduate course text. The book should prove invaluable to researchers in AI, statistics, economics, epidemiology, and philosophy, and, indeed, all those interested in the fundamental notion of causality. It may well prove to be one of the most influential books of the next decade.

Zeitouni K., A Survey of Spatial Data Mining Methods: Databases and Statistics Point of View, in Data Warehousing and Web Engineering, S. Becker, ed., IRM Press, Hershey, PA, 2002.

This chapter reviews the data-mining methods that are combined with GIS for carrying out spatial analysis of geographic data. We will first look at data-mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: The first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

13

GENETIC ALGORITHMS

Chapter Objectives

Identify effective algorithms for approximate solutions of optimization problems described with large data sets.

Compare basic principles and concepts of natural evolution and simulated evolution expressed through genetic algorithms (GAs).

Describe the main steps of a genetic algorithm with illustrative examples.

Explain standard and nonstandard genetic operators such as a mechanism for improving solutions.

Discuss a schema concept with “don’t care” values and its application to approximate optimization.

Apply a GA to the traveling-salesman problem (TSP) and optimization of classification rules as examples of hard optimizations.

There is a large class of interesting problems for which no reasonably fast algorithms have been developed. Many of these problems are optimization problems that arise frequently in applications. The fundamental approach to optimization is to formulate a single standard of measurement—a cost function—that summarizes the performance or value of a decision and iteratively improves this performance by selecting from among the available alternatives. Most classical methods of optimization generate a deterministic sequence of trial solutions based on the gradient or higher order statistics of the cost function. In general, any abstract task to be accomplished can be thought of as solving a problem, which can be perceived as a search through a space of potential solutions. Since we are looking for “the best” solution, we can view this task as an optimization process. For small data spaces, classical, exhaustive search methods usually suffice; for large spaces, special techniques must be employed. Under regular conditions, the techniques can be shown to generate sequences that asymptotically converge to optimal solutions, and in certain cases they converge exponentially fast. But the methods often fail to perform adequately when random perturbations are imposed on the function that is optimized. Further, locally optimal solutions often prove insufficient in real-world situations. Despite such problems, which we call hard-optimization problems, it is often possible to find an effective algorithm whose solution is approximately optimal. One of the approaches is based on GAs, which are developed on the principles of natural evolution.

Natural evolution is a population-based optimization process. Simulating this process on a computer results in stochastic-optimization techniques that can often outperform classical methods of optimization when applied to difficult, real-world problems. The problems that the biological species have solved are typified by chaos, chance, temporality, and nonlinear interactivity. These are the characteristics of the problems that have proved to be especially intractable to classical methods of optimization. Therefore, the main avenue of research in simulated evolution is a GA, which is a new, iterative, optimization method that emphasizes some facets of natural evolution. GAs approximate an optimal solution to the problem at hand; they are by nature stochastic algorithms whose search methods model natural phenomena such as genetic inheritance and the Darwinian strife for survival.

13.1 FUNDAMENTALS OF GAs

GAs are derivative-free, stochastic-optimization methods based loosely on the concepts of natural selection and evolutionary processes. They were first proposed and investigated by John Holland at the University of Michigan in 1975. The basic idea of GAs was revealed by a number of biologists when they used computers to perform simulations of natural genetic systems. In these systems,