Information mining purposes diversity from advertisement to social domain names, with novel functions showing quickly; for instance, in the context of social networks. The increasing program sphere and social succeed in of complex information mining bring up pertinent problems with privateness and defense. Present-day information mining is a revolutionary multidisciplinary recreation. This inter- and multidisciplinary technique is easily mirrored in the box of data platforms. the knowledge platforms learn addresses software program and standards for aiding computationally and data-intensive purposes. in addition, it encompasses reading approach and information facets, and all guide or computerized actions. In that recognize, learn on the interface of knowledge structures and knowledge mining has major power to provide actionable wisdom important for company decision-making. the purpose of the proposed quantity is to supply a balanced therapy of the most recent advances and advancements in info mining; specifically, exploring synergies on the intersection with info platforms. it's going to function a platform for teachers and practitioners to spotlight their fresh achievements and display strength possibilities within the box. due to its multidisciplinary nature, the quantity is predicted to turn into a necessary source for a huge readership starting from scholars, all through engineers and builders, to researchers and lecturers.
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Example text
However, knowledge management has not been central to data mining research so far. Data mining combined with CBR could be viewed as a general knowledge management and problemsolving tool. The resulting benefits could be better management of resources, greater efficiency and growth (Chan and Chowdhury, 2005). References Agosta, Lou. (2005). Data Warehousing Lessons Learned: Hub-and-Spoke Architecture Favored. DM Review, March 2005. , Womack, C & Yao, Y. (2006). Star or Dimensional Modeling. Assignment in Data Warehousing and Data Mining, Term Paper, WEHCBA, Roosevelt University, Summer 2006.
Chowdhury, S. (2004). Information Architecture for Electronic Customer Relationship Management. Paper presented at the American Society of Business and Behavioral Sciences (ASBBS) conference, 2004. Chowdhury S. (2007a). A Research Proposal on Model Conversion: E-R to Star Model. Roosevelt University, Spring 2007. Chan, J and Chowdhury, S. (2005). Enhancing Customer Services using Case-based Reasoning. Published in the communications of the ICISA – the official journal of the International Chinese Information Systems Association, Vol VII.
Rieger, M. L. (2006). Star Database Model: A comparison of 3 Alternatives with a Recommended Solution. Assignment in Data Warehousing and Data Mining, Term Paper WEHCBA, Roosevelt University. Sankar K. Pal, S. & Shiu C. K. (2004). Foundations of Soft Case-Based Reasoning. Wiley-IEEE, ISBN 0471086355. Sen, A. & Sinha, AP. (2005). A Comparison of Data Warehousing Methodologies. Communications of the ACM, Vol. 48, No. 3, pp. 79-84. Kimball, R and Ross, M. (2002). The Data Warehouse Toolkit. 2nd ed.