TOP-10 DATA MINING CASE STUDIES

2012 ◽  
Vol 11 (02) ◽  
pp. 389-400 ◽  
Author(s):  
GABOR MELLI ◽  
XINDONG WU ◽  
PAUL BEINAT ◽  
FRANCESCO BONCHI ◽  
LONGBING CAO ◽  
...  
Keyword(s):  
Data Mining ◽  
Case Studies ◽  
Real World ◽  
Domain Knowledge ◽  
Panel Discussion ◽  
Automated Extraction ◽  
System Failures ◽  
Data Mining Techniques ◽  
Continual Improvement ◽  
Supervised Methods

We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery.

Expanding Business Intelligence Power with System Dynamics

2011 ◽  
pp. 126-140 ◽  
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
System Dynamics ◽  
Real World ◽  
Business Intelligence ◽  
Simulation And Modeling ◽  
Business World ◽  
Data Mining Techniques ◽  
Business Dynamics ◽  
Using Data ◽  
Systems Thinking Approach

This chapter explores the opportunities to expand the forecasting and business understanding capabilities of Business Intelligence (BI) tools with the support of the system dynamics approach. System dynamics tools can enhance the insights provided by BI applications — specifically by using data-mining techniques, through simulation and modeling of real world under a “systems thinking” approach, improving forecasts, and contributing to a better understanding of the business dynamics of any organization. Since there is not enough diffusion and understanding in the business world about system dynamics concepts and advantages, this chapter is intended to motivate further research and the development of better and more powerful applications for BI.

Domain Driven Data Mining

2008 ◽  
pp. 196-223 ◽  
Author(s):  
Longbing Cao ◽  
Chengqi Zhang
Keyword(s):  
Data Mining ◽  
Complex Systems ◽  
Real World ◽  
Domain Knowledge ◽  
Pattern Mining ◽  
Iterative Refinement ◽  
User Preference ◽  
Data Driven ◽  
Real World Data ◽  
Hidden Knowledge

Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to Quantitative Intelligence focused data-driven philosophy. It either views data mining as an autonomous data-driven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learnt from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as Domain-Driven Data Mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business.

Factors influencing HER2 positivity in breast cancer according to statistical modeling and data mining techniques based on a real-world national database: HER-France.

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. e12086-e12086
Author(s):  
Marie-Pierre Chenard ◽  
Eric Anger ◽  
Marie-Helene Bizollon ◽  
Jerome Chetritt ◽  
Francesco Bruno Cutuli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Statistical Modeling ◽  
Real World ◽  
National Database ◽  
Data Mining Techniques ◽  
Factors Influencing ◽  
Her2 Positivity

scholarly journals Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia

2016 ◽  
Vol 20 ◽  
Author(s):  
Md Zahidul Islam ◽  
Steven D’Alessandro ◽  
Michael Furner ◽  
Lester Johnson ◽  
David Gray ◽  
...  
Keyword(s):  
Data Mining ◽  
Mobile Phone ◽  
Domain Knowledge ◽  
Service Providers ◽  
Mobile Service ◽  
Data Mining Techniques ◽  
Brand Switching ◽  
Switching Patterns ◽  
Switching Models ◽  
Mobile Phone Usage

There is more than one mobile-phone subscription per member of the Australian population. The number of complaints against the mobile-phone-service providers is also high. Therefore, the mobile service providers are facing a huge challenge in retaining their customers. There are a number of existing models to analyse customer behaviour and switching patterns. A number of switching models may also exist within a large market. These models are often not useful due to the heterogeneous nature of the market. Therefore, in this study we use data mining techniques to let the data talk to help us discover switching patterns without requiring us to use any models and domain knowledge. We use a variety of decision tree and decision forest techniques on a real mobile-phone-usage dataset in order to demonstrate the effectiveness of data mining techniques in knowledge discovery. We report many interesting patterns, and discuss them from a brand-switching and marketing perspective, through which they are found to be very sensible and interesting.

scholarly journals Predicting Academic Performance with Applying Data Mining Techniques (Generalizing the results of two Different Case Studies)

2014 ◽  
Vol 3 (2) ◽  
pp. 79-88 ◽  
Author(s):  
Rozita Jamili Oskouei ◽  
Mohsen Askari
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Case Studies ◽  
Teaching Method ◽  
Probability Of Failure ◽  
Data Mining Techniques

Several research works are attempted to predict students academic performance and assess  the  evaluating students knowledge  or  detecting  students’  weakness and probability of failure in final semester examinations. However, several factors affect the performance of students in different countries or even in different states of one country. Therefore, understanding these factors and analyzing the effects of each one of those factors in each country, is necessary for improving instructors’ decisions in selecting   the best teaching method for helping weak students or   increasing performance  of  other  students. This study is motivated  to  study  the  students’ academic performance in high  school  and  bachelor  degree  studies  in  Iran and comparing these analysis results with the similar study’s results in India.

Panel discussion: applications in the real world: case studies in defining boundaries and managing innovation

2007 ◽  
Vol 74 (Suppl_2) ◽  
pp. S51-S59
Author(s):  
C. R. ADKISON ◽  
M. J. MEEHAN ◽  
G. H. CASSELL ◽  
J. P. KAHN ◽  
P. A. PIZZO ◽  
...  
Keyword(s):  
Case Studies ◽  
Real World ◽  
Panel Discussion ◽  
The Real ◽  
Managing Innovation
Sign in / Sign up

Export Citation Format

Share Document