A Programmer's Guide to Data Mining
The ancient art of the Numerati provides insight into basic datamining techniques for beginners wishing to immerse themselves in the field using practical examples.
Data Mining Algorithms In R
In general terms, Data Mining comprises techniques and algorithms for determining interesting patterns from large datasets. There are currently hundreds of algorithms that perform tasks such as frequent pattern mining, clustering, and classification, among others. Understanding how these algorithms work and how to use them effectively is a continuous challenge faced by data mining analysts, researchers, and practitioners, in particular because the algorithm behavior and patterns it provides may change significantly as a function of its parameters.
This Wikibook aims to integrate three pieces of information for each technique: description and rationale, implementation details, and use cases. The description and rationale of each technique provide the necessary background for understanding the implementation and applying it to real scenarios. The implementation details not only expose the algorithm design, but also explain its parameters in the light of the rationale provided previously. Finally, the use cases provide an experience of the algorithms use on synthetic and real datasets.
Mining of Massive Datasets
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining.
This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing
Theory and Applications for Advanced Text Mining
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields.
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. This book will give new knowledge in the text mining field and help many readers open their new research fields.