NDLI: An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

Content Provider	Springer Nature Link
Author	Mojahed, Aalaa Iglesia, Beatriz
Copyright Year	2016
Abstract	This paper introduces Hk-medoids, a modified version of the standard k-medoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk-medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm, and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k-medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation.
Starting Page	27
Ending Page	52
Page Count	26
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	50
Issue Number	1
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2016-03-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Heterogeneous data k-medoids Uncertainty Data fusion Clustering SMF Information Systems and Communication Service IT in Business
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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