Diminishing downsides of Data Mining

Data Mining (DM) helps deliver tremendous insights for businesses into the problems they face and aids in identifying new opportunities. It further helps businesses to solve more complex problems and make smarter decisions. DM is a potentially powerful tool for companies; however, more research is needed to measure the benefits of DM. This paper represents a study of the effectiveness of DM in a commercial perspective. First, statistical issues are given. It is followed by data accuracy and standardisation. Diverse problems related to the information used for conducting a DM research are identified. Also, the technical challenges and potential roadblocks in an organisation itself are described.

[1]  Hsinchun Chen,et al.  EBizPort: Collecting and analyzing business intelligence information , 2004, J. Assoc. Inf. Sci. Technol..

[2]  Chris Larson Slice and Dice , 2004 .

[3]  Xiaohui Liu,et al.  Data mining from 1994 to 2004: an application-orientated review , 2005, Int. J. Bus. Intell. Data Min..

[4]  James O. Berger,et al.  The Relevance of Stopping Rules in Statistical Inference , 1988 .

[5]  D. Haussler,et al.  Boolean Feature Discovery in Empirical Learning , 1990, Machine Learning.

[6]  Steven Thorley,et al.  Mining Fool's Gold , 1999 .

[7]  Paul Gray,et al.  Special Section: Data Mining , 1999, J. Manag. Inf. Syst..

[8]  Michael J. A. Berry,et al.  Mastering Data Mining: The Art and Science of Customer Relationship Management , 1999 .

[9]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[10]  Robert Groth,et al.  Data Mining: Building Competitive Advantage , 1999 .

[11]  Xiaohua Hu,et al.  Mining novel connections from online biomedical text databases using semantic query expansion and semantic-relationship pruning , 2005, Int. J. Web Grid Serv..

[12]  Sikha Bagui An Approach to Mining Crime Patterns , 2009, Selected Readings on Database Technologies and Applications.

[13]  David Taniar,et al.  Mining Association Rules in Data Warehouses , 2005, Int. J. Data Warehous. Min..

[14]  Hyoil Han,et al.  Temporal rule induction for clinical outcome analysis , 2005, Int. J. Bus. Intell. Data Min..

[15]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[16]  Witold Abramowicz,et al.  Knowledge Discovery for Business Information Systems , 2001 .

[17]  Dorian Pyle Business modeling and data mining , 2003 .

[18]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[19]  Piotr Berman,et al.  Slice and dice , 2002, SODA 2002.

[20]  Zhengxin Chen,et al.  Data Mining and Uncertain Reasoning: An Integrated Approach , 2001 .

[21]  Siu Cheung Hui,et al.  A Web Usage Lattice Based Mining Approach for Intelligent Web Personalization , 2005, Int. J. Web Inf. Syst..

[22]  Readers' Advantage Business Modeling and Data Mining , 2003 .

[23]  Simon Parsons,et al.  Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X , 2004, The Knowledge Engineering Review.

[24]  David Taniar,et al.  Mining Parallel Patterns from Mobile Users , 2005, Int. J. Bus. Data Commun. Netw..

[25]  Ian Witten,et al.  Data Mining , 2000 .

[26]  Ishwar K. Sethi,et al.  Data mining: an introducation , 2001 .

[27]  John Wang,et al.  Data Mining: Opportunities and Challenges , 2003 .

[28]  Herna L. Viktor,et al.  Visualization Techniques for Data Mining , 2005 .

[29]  David D. Jensen Data snooping, dredging and fishing: the dark side of data mining a SIGKDD99 panel report , 2000, SKDD.

[30]  Lior Rokach,et al.  Decomposition Methodology for Knowledge Discovery and Data Mining , 2005, The Data Mining and Knowledge Discovery Handbook.