Session details: Theme: AI and agents: KomIS - Knowledge discovery meets information systems track

2020 ◽  
Author(s):  
Fabio Mercorio ◽  
Mario Mezzanzanica ◽  
Antonio Picariello
Keyword(s):  
Information Systems ◽  
Knowledge Discovery

scholarly journals Data Cleaning in Knowledge Discovery Database-Data Mining (KDD-DM)

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2196-2199
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Data Quality ◽  
Knowledge Discovery ◽  
Information Management ◽  
Data Cleaning ◽  
Main Issue ◽  
Quality Information ◽  
Data Preparation ◽  
Paper Review

Data quality is a main issue in quality information management. Data quality problems occur anywhere in information systems. These problems are solved by Data Cleaning (DC). DC is a process used to determine inaccurate, incomplete or unreasonable data and then improve the quality through correcting of detected errors and omissions. Various process of DC have been discussed in the previous studies, but there is no standard or formalized the DC process. The Domain Driven Data Mining (DDDM) is one of the KDD methodology often used for this purpose. This paper review and emphasize the important of DC in data preparation. The future works was also being highlight.

Informational Data Mining

2013 ◽  
pp. 58-65
Author(s):  
Feyza Gürbüz ◽  
Fatma Gökçe Önen
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Major Change ◽  
Research Community ◽  
Data Sources ◽  
Accurate Information ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Information Strategies

The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.

scholarly journals Rough Set Approach to Approximation Reduction in Ordered Decision Table with Fuzzy Decision

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Shihu Liu ◽  
Weihua Xu
Keyword(s):  
Information Systems ◽  
Knowledge Discovery ◽  
Rough Set ◽  
Decision Table ◽  
Fuzzy Decision ◽  
Efficient Tool ◽  
Lower Approximation ◽  
Dominance Relations ◽  
Decision Tables

In practice, some of information systems are based on dominance relations, and values of decision attribute are fuzzy. So, it is meaningful to study attribute reductions in ordered decision tables with fuzzy decision. In this paper, upper and lower approximation reductions are proposed in this kind of complicated decision table, respectively. Some important properties are discussed. The judgement theorems and discernibility matrices associated with two reductions are obtained from which the theory of attribute reductions is provided in ordered decision tables with fuzzy decision. Moreover, rough set approach to upper and lower approximation reductions is presented in ordered decision tables with fuzzy decision as well. An example illustrates the validity of the approach, and results show that it is an efficient tool for knowledge discovery in ordered decision tables with fuzzy decision.

Sign in / Sign up

Export Citation Format

Share Document