Survey of Text Mining: Clustering, Classification, and Retrieval (No. 1)
| Author | : | |
| Rating | : | 4.81 (931 Votes) |
| Asin | : | 0387955631 |
| Format Type | : | paperback |
| Number of Pages | : | 244 Pages |
| Publish Date | : | 2016-07-26 |
| Language | : | English |
DESCRIPTION:
"Five Stars" according to siamak. excellent old book. "subjective clusters" according to W Boudville. The book is relatively brief, given the technical nature of its chapters, each written by different authors. Many clustering methods are described. Most can be seen to have some degree of subjectivity, in defining what ends up in a given cluster. Or whether
It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments.This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs
Many of the chapters stress the practical application of software and algorithms for current and future needs in text mining. Authors from industry provide their perspectives on current approaches for large-scale text mining and obstacles that will guide R&D activity in this area for the next decade. Topics and features: * Highlights issues such as scalability, robustness, and software tools * Brings together recent research and techniques from academia and industry * Examines algorithmic advances in discrim