Text Mining to Support Business Intelligence - Karanikas, Haralampos
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  • Broschiertes Buch

In most companies and organisations valuable time and effort is wasted in ineffective searches through multiple information sources including web sites and other conventional sources. This problem of information overload is further exacerbated due to the unstructured format of the majority of the data. Text Mining (TM) is the emerging science and industry of applying data mining (DM), machine learning (ML), natural language processing (NLP) and information extraction (IE) to the problem of finding useful patterns hidden within large textual databases. This book offers understanding of best…mehr

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Produktbeschreibung
In most companies and organisations valuable time and effort is wasted in ineffective searches through multiple information sources including web sites and other conventional sources. This problem of information overload is further exacerbated due to the unstructured format of the majority of the data. Text Mining (TM) is the emerging science and industry of applying data mining (DM), machine learning (ML), natural language processing (NLP) and information extraction (IE) to the problem of finding useful patterns hidden within large textual databases. This book offers understanding of best practices and solutions, regarding the text mining process and its operations. It provides a unified database oriented temporal text mining framework, the DocumentMiner. DocumentMiner targets the complexity that is associated with the process of text mining by providing support for the effective analysis, management and co-ordination of the information gathered throughout the stages into a single, consistent, manageable database. This book is for everyone who wants to begin learning Text Mining to support Business Intelligence tasks or learn beyond the fundamentals.
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Autorenporträt
is currently a researcher at the University of Athens, Greece. He holds a BSc in Physics from the Aristotle University of Thessalonica, Greece, MSc in Computer Science and PhD in the field of Temporal Text Mining from the University of Manchester. He is actively involved in TM as a focal research area.