Information Loss Evaluation based on Fuzzy and Crisp Clustering of Graph Statistics
Publication Type:
Conference PaperSource:
WCCI 2012 - World Congress on Computational Intelligence, IEEE, Brisbane, Australia, p.1-8 (2012)Abstract:
In this paper we apply different types of clustering,
fuzzy (fuzzy c-Means) and crisp (k-Means) to graph statistical
data in order to evaluate information loss due to perturbation as
part of the anonymization process for a data privacy application.
We make special emphasis on two major node types: hubs, which
are nodes with a high relative degree value, and bridges, which
act as connecting nodes between different regions in the graph.
By clustering the graph's statistical data before and after
perturbation, we can measure the change in characteristics and
therefore the information loss. We partition the nodes into three
groups: hubs/global bridges, local bridges, and all other nodes.
We suspect that the partitions of these nodes are best represented
in the fuzzy form, especially in the case of nodes in frontier
regions of the graphs which may have an ambiguous assignment.
Classification of Melanomas in situ using Knowledge Discovery with Explained CBR
Publication Type:
Journal ArticleSource:
Artificial Intelligence in Medicine, Volume 51, p.12 (2011)Keywords:
knowledge discovery; classification; clustering; CBR; applicationAbstract:
The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are based on clustering methods whose goal is to analyze a set of objects and to obtain clusters based on the similarity between these objects. A desirable characteristic of clustering results is that they should be easily understandable by domain experts. In this paper we introduce LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. These explanations are the basis on which experts can perform knowledge discovery. Here we use LazyCL to generate a domain theory for classification of melanomas in situ.
Clustering-based Information Loss for Data Protection Methods of Categorical Data
Publication Type:
ThesisSource:
Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain, p.24 (2011)Keywords:
Data Privacy; Information Loss; Disclosure Risk; ClusteringAbstract:
Data privacy has been always a very important issue but it became much more important with the expansion of the Internet because, nowadays, the number of public datasets avaliable for statistical studies is growing more and more, so the amount of sensitive data available on the Internet is greater every day. This fact makes very important the assessment of the performance of all the methods used to mask those datasets. In order to check the performance there exist two kind of measures: the information loss and the disclosure risk. This performance assessment comes even more important when protecting categorical data which has a very limited manipulation.
In this thesis I present an information loss analysis based on cluster-specific measures over categorical data protection methods. That is, measures specifically defined for the case in which the user will do clustering with the data. We also compare the obtained results with the ones known using general information loss analysis.
Evaluating reliability and relevance for WOWA aggregation of Sleep Apnea case data
Publication Type:
Conference ProceedingsSource:
Congress of the European Society of Fuzzy Logic and Technology - EUSFLAT '99, Palma de Mallorca, p.283-286 (1999)Keywords:
sleep apnea diagnosis; questionnaire responses; WOWA aggregation; clustering; classification; reliability; relevance.Abstract:
In this article, joint medical and data
analysis expertise is brought to bear using
contrasting data analysis methods and the
WOWA aggregation operator to solve a
difficult medical diagnosis problem, that of
sleep apnea syndrome screening. We
describe a method of calculating the
relevance and reliability weights used by
the WOWA operator.
