Data structures are presented in a container hierarchy that includes stacks and queues as non-traversable dispensers, and lists, sets, and maps as traversable collections. Algorithm analysis is introduced and applied to linear and binary search, bubble sort, selection sort, insertion sort, merge sort and quicksort. The book also covers heaps and heapsort, unbalanced binary search trees, AVL trees, 2-3 trees, hashing, graph representations, and graph algorithms based on depth-and breadth-first search.
There are many books on data structures and algorithms, including some with useful libraries of C functions. Mastering Algorithms with C offers you a unique combination of theoretical background and working code. With robust solutions for everyday programming tasks, this book avoids the abstract style of most classic data structures and algorithms texts, but still provides all of the information you need to understand the purpose and use of common programming techniques. Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included. Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs.
Suppose that multiple experts (or learning algorithms) provide us with alternative Bayesian network (BN) structures over a domain, and that we are interested in combining them into a single consensus BN structure. Specifically, we are interested in that the consensus BN structure only represents independences all the given BN structures agree upon and that it has as few parameters associated as possible. In this paper, we prove that there may exist several non-equivalent consensus BN structures and that finding one of them is NP-hard. Thus, we decide to resort to heuristics to find an approximated consensus BN structure. In this paper, we consider the heuristic proposed by Matzkevich and Abramson, which builds upon two algorithms, called Methods A and B, for efficiently deriving the minimal directed independence map of a BN structure relative to a given node ordering. Methods A and B are claimed to be correct although no proof is provided (a proof is just sketched). In this paper, we show that Methods A and B are not correct and propose a correction of them.
Here I want to present my new book on advanced algorithms for data-intensive applications named "Probabilistic Data Structures and Algorithms in Big Data Applications" (ISBN: 9783748190486). The detailed information about the book you can find at its webpage and below I give you some introduction to the topic this book is about. There is no doubt that a lot of data creates unbelievable opportunities to learn and opens new horizons for business. However, this also implies lots of technical challenges. I guess, everyone heard about Volume, Velocity, and Variety, The Three V's of Big Data.