Emerging Technologies of Text Mining: Techniques and Applications

Massive amounts of textual data make up most organizations stored information. Therefore, there is increasingly high demand for a comprehensive resource providing practical hands-on knowledge for real-world applications. Emerging Technologies of Text Mining: Techniques and Applications provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, Emerging Technologies of Text Mining: Techniques and Applications will provide libraries with the defining reference on this topic.

[1]  Olli Simula,et al.  An approach to automated interpretation of SOM , 2001, WSOM.

[2]  W. Stephenson.  CORRELATING PERSONS INSTEAD OF TESTS , 1935 .

[3]  I. Nonaka,et al.  How Japanese Companies Create the Dynamics of Innovation , 1995 .

[4]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[5]  C. Spearman.  General intelligence Objectively Determined and Measured , 1904 .

[6]  P. Berger,et al.  The Social Construction of Reality , 1966 .

[7]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[8]  Maria Carolina Monard,et al.  A computational framework for interpreting clusters through inductive learning: a case study , 2002 .

[9]  P. Senge.  The fifth discipline : the art and practice of the learning organization/ Peter M. Senge , 1991 .

[10]  Brad Hartfield,et al.  Computer systems and the design of organizational interaction , 1988, TOIS.

[11]  R. M. Cormack,et al.  A Review of Classification , 1971 .

[12]  Hércules Antonio do Prado,et al.  An Interpretation Process for Clustering Analysis Based on the Ontology of Language , 2008 .

[13]  Stephen Jose Hanson,et al.  CONCEPTUAL CLUSTERING AND CATEGORIZATION , 1990 .

[14]  Thomas H. Davenport,et al.  Book review:Working knowledge: How organizations manage what they know. Thomas H. Davenport and Laurence Prusak. Harvard Business School Press, 1998. $29.95US. ISBN 0‐87584‐655‐6 , 1998 .

[15]  Marcelo Ladeira,et al.  Introducing Prior Knowledge Into The Clustering Process , 2003 .

[16]  P. Senge.  The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization , 2014 .

[17]  R. Sokal,et al.  Principles of numerical taxonomy , 1965 .

[18]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[19]  Marcelo Ladeira,et al.  Informed K-Means: A Clustering Process biased by Prior Knowledge , 2018, ICEIS.

[20]  H. Maturana,et al.  The Tree of Knowledge: The Biological Roots of Human Understanding , 2007 .

[21]  Vivian Weil,et al.  Owning scientific and technical information : value and ethical issues , 1991 .