Average Case Analysis Of Algorithms On Sequences

A timely book on a topic that has witnessed a surge of interest over the last decade, owing in part to several novel applications, most notably in data compression and computational molecular biology. It describes methods employed in average case analysis of algorithms, combining both analytical and probabilistic tools in a single volume. * Tools are illustrated through problems on words with applications to molecular biology, data compression, security, and pattern matching. * Includes chapters on algorithms and data structures on words, probabilistic and analytical models, inclusion-exclusion principles, first and second moment methods, subadditive ergodic theorem and large deviations, elements of information theory, generating functions, complex asymptotic methods, Mellin transform and its applications, and analytic poissonization and depoissonization. * Written by an established researcher with a strong international reputation in the field.

A careful and cogent analysis of the average-case behavior of a variety of algorithms accompanied by mathematical calculations. The analysis consists of determining the behavior of an algorithm in the best, worst, and average case. Material is outlined in various exercises and problems.

Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Data Structures & Theory of Computation

Repetitive structures in biological sequences are emerging as an active focus of research and the unifying concept of "repeatome" (the ensemble of knowledge associated with repeating structures in genomic/proteomic sequences) has been recently proposed in order to highlight several converging trends. One main trend is the ongoing discovery that genomic repetitions are linked to many biological significant events and functions. Diseases (e.g. Huntington's disease) have been causally linked with abnormal expansion of certain repeating sequences in the human genome. Deletions or multiple copy duplications of genes (Copy Number Variations) are important in the aetiology of cancer, Alzheimer, and Parkinson diseases. A second converging trend has been the emergence of many different models and algorithms for detecting non-obvious repeating patterns in strings with applications to in genomic data. Borrowing methodologies from combinatorial pattern, matching, string algorithms, data structures, data mining and machine learning these new approaches break the limitations of the current approaches and offer a new way to design better trans-disciplinary research. The articles collected in this book provides a glance into the rich emerging area of repeatome research, addressing some of its pressing challenges. We believe that these contributions are valuable resources for repeatome research and will stimulate further research from bioinformatic, statistical, and biological points of view.

This book constitutes the refereed proceedings of the 15th Annual Symposium on Combinatorial Pattern Matching, CPM 2004, held in Istanbul, Turkey in July 2004. The 36 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are devoted to current theoretical and computational aspects of searching and matching of strings and more complicate patterns, such as trees, regular expressions, graphs, point sets, and arrays. Among the application fields addressed are computational biology, bioinformatics, genomics, proteinomics, the web, data compression, coding, multimedia, information retrieval, data analysis, pattern recognition, and computer vision.

This book constitutes the refereed proceedings of the 16th Annual Symposium on Combinatorial Pattern Matching, CPM 2005, held in Jeju island, Korea on June 19-22, 2005. The 37 revised full papers presented were carefully reviewed and selected from 129 submissions. They constitute original research contributions in combinatorial pattern matching and its applications. Among the application fields addressed are computational biology, bioinformatics, genomics, proteinomics, data compression, Sequence Analysis and Graphs, information retrieval, data analysis, and pattern recognition.