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K -Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-16
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data.
In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI) for incomplete data.
Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data.
The similarity function can be changed by dealing with the interval data.
Then the improved AP algorithm can be applicable to the case of incomplete data.
Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.
American Psychological Association (APA)
Lu, Cheng& Song, Shiji& Wu, Cheng. 2015. K -Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1074073
Modern Language Association (MLA)
Lu, Cheng…[et al.]. K -Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data. Mathematical Problems in Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1074073
American Medical Association (AMA)
Lu, Cheng& Song, Shiji& Wu, Cheng. K -Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1074073
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074073