Publication
Title
Efficient cluster detection by ordered neighborhoods
Author
Abstract
Detecting cluster structures seems to be a simple task, i.e. separating similar from dissimilar objects. However, given today's complex data, (dis-)similarity measures and traditional clustering algorithms are not reliable in separating clusters from each other. For example, when too many dimensions are considered simultaneously, objects become unique and (dis-) similarity does not provide meaningful information to detect clusters anymore. While the (dis-) similarity measures might be meaningful for individual dimensions, algorithms fail to combine this information for cluster detection. In particular, it is the severe issue of a combinatorial search space that results in inefficient algorithms. In this paper we propose a cluster detection method based on the ordered neighborhoods. By considering such ordered neighborhoods in each dimension individually, we derive properties that allow us to detect clustered objects in dimensions in linear time. Our algorithm exploits the ordered neighborhoods in order to find both the similar objects and the dimensions in which these objects show high similarity. Evaluation results show that our method is scalable with both database size and dimensionality and enhances cluster detection w.r.t. state-of-the-art clustering techniques.
Language
English
Source (journal)
Lecture notes in computer science. - Berlin, 1973, currens
Source (book)
17th International Conference on Big Data Analytics and Knowledge, Discovery (DaWaK), SEP 01-04, 2015, Valencia, SPAIN
Publication
Berlin : Springer-verlag berlin, 2015
Volume/pages
9263(2015), p. 15-27
ISI
000363583200002
Number
978-3-319-22729-0
978-3-319-22728-3
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 09.12.2015
Last edited 09.06.2017
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