Every major Peruvian author of the twentieth century has written a narrative focused on childhood or coming of age. Mining Memory argues that Peruvian narratives of the twentieth century re-imagine childhood not only to document personal pasts, but also to focus on national identity as a dynamic and incomplete process. Mining Memory shows how 20th-century narratives and films reimagine the self and the nation by representing child and adolescent protagonists and their evolution, using the remembrance of childhood as part of a nation-making project. The book demonstrates how, in the context of Peru, fictions focusing on childhood become vehicles for the national reimagining and collective remembering central to much of Latin American literature. The figure of the child, as emblem of both a collective memory and an always deferred utopian project, holds special promise for twentieth-century Peruvian writers as they write from a national context rife with cultural, racial and political conflict. The book intervenes in debates internal to Peruvian cultural studies as well as wider conversations in Latin American Studies and post-colonial studies. Mining Memory provides a new understanding to both the Latin American and Anglo-American traditions regarding the representations of national subjectivities through the voices of the child and adolescent. Such a representational strategy performs a very particular kind of hybridity and temporal balancing act capable of addressing the very issues of cultural memory and fractured identities so relevant to multi-cultural, post-colonial cultural contexts.

Over the past five hundred years, North Americans have increasingly turned to mining to produce many of their basic social and cultural objects. From cell phones to cars and roadways, metal pots to wall tile and even talcum powder, mineral-intensive products have become central to modern North American life. As this process has unfolded, mining has also indelibly shaped the natural world and North Americans’ relationship with it. Mountains have been honeycombed, rivers poisoned, and forests leveled. The effects of these environmental transformations have fallen unevenly across North American societies. Mining North America examines these developments. Drawing on the work of scholars from Mexico, the United States, and Canada, this book explores how mining has shaped North America over the last half millennium. It covers an array of minerals and geographies while seeking to draw mining into the core debates that animate North American environmental history generally. Taken together, the authors' contributions make a powerful case for the centrality of mining in forging North American environments and societies.

This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data. The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.

Billy Ray was the name his mother chose for him, but the doctor insisted his birth certificate read “William.” Knowing his mother, Billy Ray is sure she shared some choice words with the man, as “William” has been called “Billy Ray” ever since while growing up and living in the small mining town of Minden, West Virginia, where stories grow like trees. Mining Town Memories is a collection of poems that tell the tall and small tales of those living and dying near the New River. This is a look into the everyday life of the miner, how he strives to work under difficult conditions, surviving in and outside the mine. Families had to survive, too, on the little money earned, making extra effort to provide for their needs. A close insight into the mining life, this collection portrays the mental and emotional state of a hard-working band of brothers. Many shouted from the mines for God’s protection. Some souls were lost, while othes saved. The life of a miner is a life like no other—one of darkness and strain but also hope and light, revealed now for the first time in poetic verse.

The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.

Ever since the early days of machine learning and data mining, it has been realized that the traditional attribute-value and item-set representations are too limited for many practical applications in domains such as chemistry, biology, network analysis and text mining. This has triggered a lot of research on mining and learning within alternative and more expressive representation formalisms such as computational logic, relational algebra, graphs, trees and sequences. The motivation for using graphs, trees and sequences. Is that they are 1) more expressive than flat representations, and 2) potentially more efficient than multi-relational learning and mining techniques. At the same time, the data structures of graphs, trees and sequences are among the best understood and most widely applied representations within computer science. Thus these representations offer ideal opportunities for developing interesting contributions in data mining and machine learning that are both theoretically well-founded and widely applicable. The goal of this book is to collect recent outstanding studies on mining and learning within graphs, trees and sequences in studies worldwide.