Algorithms Books

Data mining methods continue to improve, and this book gives you a good sense of where the field is currently at. A wide gamut of ideas.

For example, there is a good discussion of new clustering techniques. Closely related, and in practice inseparable, are visualisation methods that can be applied to such clusters. The sheer mass of information in the clusters makes strong visualisation a necessity for a manual comprehension of the data. If nothing else, it can be used to see if the clusters make sense, in the context of your application. The text describes an example implementation, to retail data. But a careful reading of the methods show that they are potentially quite general.

First of all, this is a compilation of works by multiple authors, so the flow is quite illogical. There is a lot of good info here, you just have to poke around a bit. This is great for anyone who has a good notion of what a genetic algorithm is, but wants to take it a bit further and get some inspiration, along with practical tips.

This is a collection of papers presented at a NIPS workshop held in 1997. So it provides a good entry point for access to forefronts of this rapidly developing field. Many leading researchers have contributed to this volume including V. vapnik who wrote a very succinct and readable survey. The introduction (Chapter 1) is also very useful. Though all chapters are written by leading experts in their areas and are enjoy to read. Personally I like particularly Part II on implementation in large data sets. G. Wahba provides some background on RKHS theory and a statistical perspective from GACV, for which she is mainly responsible for its popularity in statistics. I recommend this book for researchers and practitioners who may want more details and update recent developments.

This hard cover volume contains the papers presented at an international conference on matrix analytic methods. Matrix analytic methods are a body of techniques developed for algorithmic analysis of wide classes of applied probability models.

The papers in the volume are all of excellent quality and attest to the thorough refereeing/selection process the submissions should have been subjected to. Several of the papers report on what one may consider to be signficant and major advances in the field. In addition, there are several papers reporting on practical applications of the method.

The book is certainly a useful addition to the literature and has signficant value as a reference. The hard cover volume has a price that is commensurate with the quality of both its content and presentation.

Algorithmics for Hard Problems addresses an important (if somewhat small) group of technical people who work exclusively on intractable problems. It takes a great deal of creativity and a large set of intellectual tools to make even modest progress on these problems. Prof. Hromkovic's book presents and organizes a big package of ideas and tools for approaching these problems. Even the last section, entitled "A Guide to Solving Hard Problems" addresses some of the practical issues of working on problems for which a completely satisfactory answer may be out of reach.

This book is especially helpful for software professionals solving difficult problems because it helps one to categorize and understand where the "pain points" can be found. In many software applications very hard problems are hidden within perfectly reasonable appearing and seemingly benign systems -- it's critical in these cases to correctly project the details of the problem onto a well defined set of basis tasks.

I THINK, THE BOOK IS VERY INTERESTING AND IS IMPORTANT FOR DEVELOPER OF MY TESIS

If you are a computer science student or advance programmer, you should get this book. The diagrams and descriptions in the book give you insights of the advance programming which would help you. However you may get confused because the examples in the book is using the programming language Eiffel. You would like this book if and only if you like hard coding.

The book is easy to read and it introduces the reader into the world of algorithms and data structures. It's like a cookbook for computer science students. Very nice!

2.1.1 Newton's method 98
2.1.2 Stabilization methods 101
2.2 Computation of Real Schur Form and Invariant Subspaces 105
2.2.1 Basic definitions and properties 105
2.2.2 Preprocessing algorithms 114
2.2.3 The QR algorithm 118
2.2.4 Real Schur form computation and ordering 135
2.3 Solving Sylvester and Lyapunov Equations 143
2.3.1 Solving Sylvester equations 144
2.3.2 Solving Lyapunov equations 159
2.3.3 Solving stable non-negative definite Lyapunov equations 162
2.4 Stabilization Algorithms 174
2.4.1 Full stabilization algorithms 174
2.4.2 Partial stabilization algorithms 177
2.5 Newton-Based Riccati Solvers 179
2.5.1 Algorithmic templates 180
2.5.2 Computational issues 183
2 5.3 Applicability and limitations 186
References 191
3 Schur and Generalized Schur Algorithms 197

I'm pleased with the text in this book; the descriptions are almost all clear, and reading through the book gives me insight into some more interesting problems in trees and graphs. The high points of the book are its treaments of tree and graph isomorphism, but I also found the discussions of non-traditional traversal algorithms on trees and graphs very interesting. The author discussions leaf-first, breadth-first, and depth-first traversals and provides algorithms for their implementation. Many of the algorithms include correctness proofs.

These topics alone made the book worth its to me. A deep academic book that costs less than $50 is nearly unheared-of.

Unfortunately, there are two flaws that make the book hard to use. In a nutshell, the author expects the reader to buy into a couple of pretty invasive and expensive propositions.

First, the author decided to use literate programming for all of his presented algorithms and code fragments. This isn't so bad, since literate programming is about documenting code. If you suppose that the author wrote the code, then documented it, then calld it a book, using a tool like literate programming seems like a natural choice.

But if you're not familiar with literate programming, it's a bit of a chore. The author's introduction to literate programming doesn't help with some of the questions even an experienced programmer might have wend reading the text. More practically, literate programming enforces operators that are different than most C/C++ developers are familiar with, and can cause confusion when reading the text. ^ is used, for example, to indicate a logical and, where C/C++ developers expect it to indicate a bitwise-exclusive or.

While it's esay to eventually overcome such tricks of memory, I've been finding it hard to scan literate programs to find definitions and declarations. The author doesn't include a CD (and at this cover price, that is hard to fault) but also doesn't make his code available for download. His website includes a LEDA-based program that interactively demonstrates some algorithms, but doesn't include code for the algorithm his own books develops and discusses.

The other decision made by the author, to the overwhelming inconvenience of this reader, is the reliance on the LEDA library for his samples and programs. The algorithms are understandable without the library, but a reader without access to LEDA doesn't benefit from any of the visualizations the author provides. , and several include

In fact, the author spends about 40 pages discussing LEDA and the characteristics of its implementation. Maybe researchers working on tree and graph algorithms all use LEDA and have ready access to it, but the literate programming code provided to for some of the algorithms isn't useful to readers who aren't familiar with LEDA, as researching the definitions and declarations themsleves becomes arduous.

The bibliography is very diverse, with more than 380 entries and is well-cited throughout the book. Unfortunately, the author sometimes relies on the bibliography too much. On page 392, the author brings up "the so-called graph isopomorphism disease" without defining it himself; he instead relies on his bibliography entries to give the user any definition or background on the "disease".

Unfortunately, the index received not nearly as much attention as the bibliography; neglecting whitespace, it's scarcely more than a single page long!

The book appears to be something more than a research paper, but is written much like a research apper would be. The book probably also serves well for a class that teaches this subject, and assumes LEDA and literate programming as prerequisites. But as a commercial developer who's interested in applying advanced graph and tree algorithms to the work I'm doing, I found the book has limited its value by relying on LEDA and applying literate programming.

All this said, I still feel it's appropriate to give the book four stars. The material covered is hard to find elsewhere, and with some effort I can overcome the LEDA and literate programming hurdles. Since I don't use LEDA or literate programming day-to-day, I'll have to overcome that unfamiliarity every time I pick up the book as a reference.