Data mining for the enterprise

Kleissner, Charly

doi:10.1109/hicss.1998.649224

Cited by 38 publications

(12 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Birkelbach, 1998), reachability, a general lack of willingness to cooperate and a decreasing interest in the topic of the survey over time are particularly relevant reasons for dropouts. The majority of dropouts can therefore be linked to some pattern that can be identified by using advanced statistical techniques, for instance, classification methods such as those used for churn prediction in the telecom sector (Kleissner, 1998;Ferreira et al, 2004).…”

Section: Panel Surveysmentioning

confidence: 99%

Panel surveys go mobile

Weber

Denk²,

Oberecker³

et al. 2008

IJMC

View full text Add to dashboard Cite

In order to take panel surveys into the 'mobile age', a careful analysis of their characteristics is conducted. Core issues of mobile surveys and panel designs are addressed and contrasted to their respective alternatives. Moreover, the use of mobile technology for carrying out panel surveys is discussed. Thereby, necessary actions and actors involved are identified, which provides a guideline for realising the potential of 'mobile panel surveys'. Essentially, the benefits are due to enhancements in the areas of survey quality, management and technology. The recommendations made can contribute to the development of a new business model in market research.

show abstract

Section: Panel Surveysmentioning

confidence: 99%

Panel surveys go mobile

Weber

Denk²,

Oberecker³

et al. 2008

IJMC

View full text Add to dashboard Cite

show abstract

“…Some emphasise that data mining is capable of revealing useful information (Berry & Linoff 1995;Chien & Chen 2008). Knowledge of patterns or rules in a company's database, for instance, could enable the executives to generate new proposals or make more effective decisions (Keissner 1998). Other scholars stress the hidden nature of the correlations: what is revealed in data mining tends to be what was previously unknown to the experts (Grupe & Owrang 1995).…”

Section: Data Miningmentioning

confidence: 99%

Mining Association Rules between Abnormal Health Examination Results and Outpatient Medical Records

Huang

2013

HIM J

View full text Add to dashboard Cite

Currently, interpretation of health examination reports relies primarily on the physician's own experience. If health screening data could be integrated with outpatient medical records to uncover correlations between disease and abnormal test results, the physician could benefit from having additional reference resources for medical examination report interpretation and clinic diagnosis. This study used the medical database of a regional hospital in Taiwan to illustrate how association rules can be found between abnormal health examination results and outpatient illnesses. The rules can help to build up a disease-prevention knowledge database that assists healthcare providers in follow-up treatment and prevention. Furthermore, this study proposes a new algorithm, the data cutting and sorting method, or DCSM, in place of the traditional Apriori algorithm. DCSM significantly improves the mining performance of Apriori by reducing the time to scan health examination and outpatient medical records, both of which are databases of immense sizes.

show abstract

“…Kleissner (1998) suggests that a knowledge discovery cycle should consist of the following four steps: 1. data selection; 2. data cleaning; 3. data conversion and interpretation; 4. data mining.…”

Section: Data Miningmentioning

confidence: 99%

Design of a product quality control system based on the use of data mining techniques

Wu¹,

Chen²,

Chian³

2006

IIE Transactions

View full text Add to dashboard Cite

In recent years industries have increasingly used customer relationship labor in management as a tool to improve their position in the marketplace. This research involves using a data warehouse, decision-tree-based data mining, and neural network pattern classification analysis to isolate the causes of non-conformity in IC packaging. The correctness of the classification analysis produced using the two methods is compared. Our objective is to establish an information analysis system, that is able to quickly identify the causes of problems thereby reducing the time taken to solve quality-related problems. It is shown that predictions made about the target group using decision tree analysis are more accurate than those made by neural network classification, indicating that decision tree analysis is an effective means of classification analysis of a company's quality problems.

show abstract

Data mining for the enterprise

Cited by 38 publications

References 1 publication

Panel surveys go mobile

Panel surveys go mobile

Mining Association Rules between Abnormal Health Examination Results and Outpatient Medical Records

Design of a product quality control system based on the use of data mining techniques

Contact Info

Product

Resources

About