Real World Data Mining Applications [Paperback]

Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring synergies at the intersection with information systems. It will serve as a platform for academics and practitioners to highlight their recent achievements and reveal potential opportunities in the field. Thanks to its multidisciplinary nature, the volume is expected to become a vital resource for a broad readership ranging from students, throughout engineers and developers, to researchers and academics.?

Introduction.- What Data Scientists can Learn from History.- On Line Mining of Cyclic Association Rules From Parallel Dimension Hierarchies.- PROFIT: A Projected Clustering Technique.- Multi-Label Classification with a Constrained Minimum Cut Model.- On the Selection of Dimension Reduction Techniques for Scientific Applications.- Relearning Process for SPRT In Structural Change Detection of Time-Series Data.- K-means clustering on a classifier-induced representation space: application to customer contact personalization.- Dimensionality Reductiló>