Paper Info
Title
Knowledge Acquisition Approach Based On Incremental Objects From Data With Missing Values
Abstract
Knowledge acquisition is the process of extracting useful knowledge from data sets to analyze data in areas of data mining and knowledge discovery. Most current knowledge acquisition work mainly focuses on static data. However, due to the dynamic characteristics of data, the objects grow at an unprecedented rate in real-world data sets. The incremental objects with a dynamic environment significantly affect knowledge updating. To maintain the effectiveness of knowledge from the dynamic data, it is necessary to update the knowledge timely. So far, there are relatively few studies on knowledge acquisition for the data with missing feature values, i.e., incomplete data. To handle with this issue, an incremental updating manner of the accuracy matrix and coverage matrix are first proposed on the basis of the computations of the tolerance classes in incomplete data, which plays an important role in the knowledge acquisition process. Then, an incremental knowledge acquisition algorithm is proposed when some new objects added to the data with missing values. Finally, some numerical experiments are conducted to evaluate the efficiency of the proposed algorithm.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2913312
IEEE ACCESS
Keywords
Field
DocType
Knowledge acquisition, incremental objects, incomplete decision system, granular computing, tolerance relation
Data mining,Data set,Static data,Matrix (mathematics),Computer science,Dynamic data,Knowledge extraction,Missing data,Knowledge acquisition,Distributed computing,Computation
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Wenhao Shu112311.98
Wenbin Qian2146.54
Yonghong Xie312214.43