λ-diverse nearest neighbors browsing for multidimensional data
| dc.citation.epage | 493 | en_US |
| dc.citation.issueNumber | 3 | en_US |
| dc.citation.spage | 481 | en_US |
| dc.citation.volumeNumber | 25 | en_US |
| dc.contributor.author | Kucuktunc, O. | en_US |
| dc.contributor.author | Ferhatosmanoglu, H. | en_US |
| dc.date.accessioned | 2015-07-28T12:00:13Z | |
| dc.date.available | 2015-07-28T12:00:13Z | |
| dc.date.issued | 2013-03 | en_US |
| dc.department | Department of Computer Engineering | en_US |
| dc.description.abstract | Traditional search methods try to obtain the most relevant information and rank it according to the degree of similarity to the queries. Diversity in query results is also preferred by a variety of applications since results very similar to each other cannot capture all aspects of the queried topic. In this paper, we focus on the -diverse k-nearest neighbor search problem on spatial and multidimensional data. Unlike the approach of diversifying query results in a postprocessing step, we naturally obtain diverse results with the proposed geometric and index-based methods. We first make an analogy with the concept of Natural Neighbors (NatN) and propose a natural neighbor-based method for 2D and 3D data and an incremental browsing algorithm based on Gabriel graphs for higher dimensional spaces. We then introduce a diverse browsing method based on the distance browsing feature of spatial index structures, such as R-trees. The algorithm maintains a Priority Queue with mindivdist of the objects depending on both relevancy and angular diversity and efficiently prunes nondiverse items and nodes. We experiment with a number of spatial and high-dimensional data sets, including Factual’s (http://www.factual.com/) US points-of-interest data set of 13M entries. On the experimental setup, the diverse browsing method is shown to be more efficient (regarding disk accesses) than k-NN search on R-trees, and more effective (regarding Maximal Marginal Relevance (MMR)) than the diverse nearest neighbor search techniques found in the literature. | en_US |
| dc.identifier.doi | 10.1109/TKDE.2011.251 | en_US |
| dc.identifier.issn | 1041-4347 | |
| dc.identifier.uri | http://hdl.handle.net/11693/12136 | |
| dc.language.iso | English | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1109/TKDE.2011.251 | en_US |
| dc.source.title | IEEE Transactions on Knowledge & Data Engineering | en_US |
| dc.subject | Diversity | en_US |
| dc.subject | Diverse nearest neighbor search | en_US |
| dc.subject | Angular similarity | en_US |
| dc.subject | Natural neighbors | en_US |
| dc.subject | Gabriel graph | en_US |
| dc.title | λ-diverse nearest neighbors browsing for multidimensional data | en_US |
| dc.type | Article | en_US |
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