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Chapter and Conference Paper
Knowledge Discovery from Noisy Datasets
It is a significant challenges to deal with the noise data in data mining and knowledge discovery applications. Most of previous works on data cleansing and correction have been focused on addressing class noi...
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Chapter and Conference Paper
On Kernel Information Propagation for Tag Clustering in Social Annotation Systems
In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptors. Tags are used to index, annotate and retrieve resource as an additional metadata of resour...
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Chapter and Conference Paper
A Local Information Passing Clustering Algorithm for Tagging Systems
Under social tagging systems, a typical Web2.0 application, users label digital data sources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource a...
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Chapter and Conference Paper
APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags
In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resou...
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Chapter and Conference Paper
Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach
Web clustering is an approach for aggregating Web objects into various groups according to underlying relationships among them. Finding co-clusters of Web objects is an interesting topic in the context of Web ...
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Chapter and Conference Paper
Co-clustering for Weblogs in Semantic Space
Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in t...
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Chapter and Conference Paper
A Creditable Subspace Labeling Method Based on D-S Evidence Theory
Due to inherent sparse, noise and nearly zero difference characteristics of high dimensional data sets, traditional clustering methods fails to detect meaningful clusters in them. Subspace clustering attempts ...
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Chapter and Conference Paper
Data Set Homeomorphism Transformation Based Meta-clustering
Clustering analysis is an important data mining technique with a variety of applications. In this paper, the data set is treated in a dynamic way and a Data Set Homeomorphism Transformation Based Meta-Clusteri...
