Semi-Supervised Clustering

2015 ◽  
pp. 464-489
Keyword(s):  
Supervised Clustering

scholarly journals Feature selection and semi-supervised clustering using multiobjective optimization

SpringerPlus ◽  
2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Sriparna Saha ◽  
Asif Ekbal ◽  
Abhay Kumar Alok ◽  
Rachamadugu Spandana
Keyword(s):  
Feature Selection ◽  
Multiobjective Optimization ◽  
Supervised Clustering

scholarly journals Supervised clustering of label ranking data using label preference information

2013 ◽  
Vol 93 (2-3) ◽  
pp. 191-225 ◽  
Author(s):  
Mihajlo Grbovic ◽  
Nemanja Djuric ◽  
Shengbo Guo ◽  
Slobodan Vucetic
Keyword(s):  
Ranking Data ◽  
Preference Information ◽  
Supervised Clustering ◽  
Label Ranking

scholarly journals An Incremental Classification Algorithm for Mining Data with Feature Space Heterogeneity

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Feature Space ◽  
Classification Problem ◽  
Classification Algorithm ◽  
Data Sets ◽  
Real World Data ◽  
Supervised Clustering ◽  
Online Classification ◽  
Efficiency And Effectiveness ◽  
Feature Relevance ◽  
Incremental Classification

Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH), to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

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