Digital Rush: Nine Internet Start-Ups in the Race for Dot.Com Riches

The only rule for Web success is that there are no rules. The dot- com entrepreneurs who make it big are the ones who forge their own paths and hammer out their own strategies. But where can a Web start-up or spinout go for reliable guidance, other than the school of virtual hard knocks?

DIGITAL RUSH explores the experiences and ideas behind some of the most fascinating and fast-growing Internet businesses of our time. Unlike other Web profile books, it digs deep by concentrating on a select group of cyber- entrepreneurs.

And they’re hardly the usual dot-com suspects. Readers go behind the scenes with innovators such as: Startups.com, VarsityBooks.com, Enews.com, LivePrint.com, foofoo.com, RocketRadio, and others. The people who created, financed, and sweat blood over these sites talk about their dumbest mistakes, their smartest moves, how to cut through the clutter, and what they’d do differently if they were starting today.

This candid and business-savvy book delivers invaluable insights on how to: Start an Internet company * Take it to greater levels * Add an e-component to a brick-and-mortar business * Secure capital at every stage * Find talent * Attend to customer service * Generate new ideas * Forecast future Internet trends, and more.

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SAP: Inside the Secret Software Power

SAP came out of nowhere to become the world’s second largest software company. Its stock rose 3700% in five years. It’s made the cover of Fortune and other global business magazines. Everyone, including Bill Gates, is in awe of this Germany-based giantand at last they can learn what makes SAP tick.

In SAP: Inside the Secret Software Power, an internationally technology reporter takes an in-depth and penetrating look at SAP’s founders, employees, customers, critics, competitors, and strategies. He profiles the company’s meteoric rise in a real-life tale of power and intrigue.

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Metaheuristic Clustering (Studies in Computational Intelligence)

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention.

In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges.

Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

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