A Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Hausdorff Distance

2007 ◽  
pp. 35-44
Author(s):  
Francisco de A. T. de Carvalho
Keyword(s):  
Hausdorff Distance ◽  
Clustering Algorithm ◽  
Interval Data ◽  
Symbolic Interval Data

IPFCM Clustering Algorithm Under Euclidean and Hausdorff Distance Measure for Symbolic Interval Data

2019 ◽  
Vol 21 (7) ◽  
pp. 2102-2119 ◽  
Author(s):  
Jin-Tsong Jeng ◽  
Chih-Ming Chen ◽  
Sheng-Chieh Chang ◽  
Chen-Chia Chuang
Keyword(s):  
Hausdorff Distance ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Interval Data ◽  
Symbolic Interval Data

scholarly journals K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng Lu ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Clustering Analysis ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Interval Data ◽  
Similarity Function ◽  
K Nearest Neighbor ◽  
Partial Data ◽  
Missing Attributes ◽  
Ap Clustering

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 onK-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.

Sign in / Sign up

Export Citation Format

Share Document