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Managing and Mining Graph Data (Advances in Database Systems)Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific... | | Learning PHP & MySQL: Step-by-Step Guide to Creating Database-Driven Web SitesPHP and MySQL are quickly becoming the de facto standard for rapid development of dynamic, database-driven web sites. This book is perfect for newcomers to programming as well as hobbyists who are intimidated by harder-to-follow books. With concepts explained in plain English, the new edition starts with the basics of the PHP language, and explains... | | JBoss Drools Business RulesIn business, a lot of actions are trigged by rules: "Order more ice cream when the stock is below 100 units and temperature is above 25° C", "Approve credit card application when the credit background check is OK, past relationship with the customer is profitable, and identity is confirmed", and so on. Traditional... |
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An Introduction to Neural NetworksCovers: artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps.
This book grew out of a set of course notes for a neural networks module given as part of a... | | Design Patterns in C#: A Hands-on Guide with Real-World Examples
Get hands-on experience with each Gang of Four design pattern using C#. For each of the patterns, you’ll see at least one real-world scenario, a coding example, and a complete implementation including output.
In the first part of Design Patterns in C#, you will cover the 23 Gang of Four (GoF) design ... | | Mining Sequential Patterns from Large Data Sets (Advances in Database Systems)The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its... |
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