scholarly journals Editorial: Data Mining Lessons Learned

2004 ◽  
Vol 57 (1/2) ◽  
pp. 5-11 ◽  
Author(s):  
Nada Lavrač ◽  
Hiroshi Motoda ◽  
Tom Fawcett
Keyword(s):  
Data Mining ◽  
Lessons Learned

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

Lessons Learned From Data Mining, Decision Support and Collaboration

2003 ◽  
pp. 237-246
Author(s):  
Dunja Mladenić ◽  
Nada Lavrač ◽  
Marko Bohanec
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Lessons Learned

Lessons learned from contests in data mining

2011 ◽  
Author(s):  
Charles Elkan ◽  
Jeremy Howard ◽  
Yehuda Koren ◽  
Tie-Yan Liu ◽  
Claudia Perlich
Keyword(s):  
Data Mining ◽  
Lessons Learned

Caffeine Intake, Race, and Risk of Invasive Breast Cancer Lessons Learned from Data Mining a Clinical Database

2006 ◽  
Author(s):  
S. Maskery ◽  
Yonghong Zhang ◽  
Hai Hu ◽  
C. Shriver ◽  
J. Hooke ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Invasive Breast Cancer ◽  
Lessons Learned ◽  
Caffeine Intake ◽  
Clinical Database

scholarly journals Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup

2003 ◽  
Vol 19 (Suppl 1) ◽  
pp. i331-i339 ◽  
Author(s):  
A. S. Yeh ◽  
L. Hirschman ◽  
A. A. Morgan
Keyword(s):  
Data Mining ◽  
Lessons Learned ◽  
Text Data ◽  
Text Data Mining ◽  
Database Curation

Non-Traditional Data Mining Applications in Taiwan National Health Insurance (NHI) Databases

2017 ◽  
Vol 12 (4) ◽  
pp. 31-51
Author(s):  
Joseph Tan ◽  
Fuchung Wang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Health Insurance ◽  
Decision Support ◽  
National Health Insurance ◽  
National Health ◽  
Domain Knowledge ◽  
Lessons Learned ◽  
Application Systems ◽  
Taiwan National Health Insurance

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance (NHI) databases. In order to obtain the best payment management, a hybrid mining (HM) approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytic processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will assist in directing the health insurance decision-making process, is built. Drawing from lessons learned within a case study setting, results showed that not only is HM approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Essentially, HM approach can provide a critical boost to health insurance decision support; hence, future researchers should develop and improve the approach combined with their own application systems.

Mining Big Data and Streams

2018 ◽  
pp. 406-417
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

Data Warehousing and Data Mining Lessons for EC Companies

2008 ◽  
pp. 381-388
Author(s):  
Neerja Sethi ◽  
Vijay Sethi
Keyword(s):  
Data Mining ◽  
Performance Monitoring ◽  
Data Warehousing ◽  
Lessons Learned ◽  
Customer Relationship ◽  
Fine Tuning ◽  
Management Support ◽  
Web Performance ◽  
Unified View ◽  
Customer Profiles

Internet companies are now in the second stage of evolution in which the emphasis is on building brands (Campman, 2001) and retaining customers rather than just transactions. There is also an imperative for multidimensional Web performance monitoring (Earls, 2005) and a continual fine-tuning of sites for optimal navigation, increased stickiness and transactional efficiency. Such research as the relationship between customer profiles and navigational characteristics (Garatti, Sergio, Sergio, & Broccab, 2004) and techniques for seamlessly aggregating Web data with corporate data (Wood & Ow, 2005) also testify to the importance of holistic data analysis for knowledge discovery. The technologies that are becoming critical in this fight for customer retention are data warehousing, data mining and customer relationship management. This article presents two case studies, one on data warehousing and the other on data mining, to draw some very specific lessons about management support, organizational commitment and overall implementation of such projects. These lessons complement past recommendations that these technologies are more about organization change (Kale, 2004), about a single unified view of the business and, ultimately about building a shared data model of the enterprise. We start with a brief overview of data warehousing and data mining. The two cases are discussed next, using a similar analytical structure to facilitate comparison among them. In the conclusion, we describe the key lessons learned from the two cases and implication

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