The k-Nearest Neighbour Join: Turbo Charging the KDD Process

Böhm, Christian; Krebs, Florian

doi:10.1007/s10115-003-0122-9

Cited by 126 publications

(109 citation statements)

References 16 publications

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“…It is shown in the experiments in [6,9] that the use of an index structure can considerably speed up kNN join processing. One of the first publications discussing kNN join algorithms was [1]. In this paper, Böhm et al demonstrate that kNN joins are useful database primitives that can be employed to speed up various data mining algorithms like k-Means clustering.…”

Section: Related Workmentioning

confidence: 98%

“…However, in many applications like data mining and similarity search, it is previously known that it is necessary to process a large number of kNN queries to generate a result. More precisely, an AkNN query retrieves the knearest neighbors in the inner set or database S for each object in the outer or query set R. Let us note that the same type of query is also known as kNN join [1].…”

Section: Introductionmentioning

confidence: 99%

“…An AkNN query efficiently retrieves the best matches for a set of local descriptors in a given image database. Another area of application is data mining: [1] surveys multiple data mining algorithms that can be accelerated by efficient methods for AkNN computation like parallel kNN classification and k-means clustering. Furthermore, it is possible to employ AkNN processing for deriving outlier factors like [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing All-Nearest-Neighbor Queries with Trigonometric Pruning

Emrich

Graf

Kriegel

et al. 2010

Lecture Notes in Computer Science

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Abstract. Many applications require to determine the k-nearest neighbors for multiple query points simultaneously. This task is known as all-(k)-nearest-neighbor (AkNN) query. In this paper, we suggest a new method for efficient AkNN query processing which is based on spherical approximations for indexing and query set representation. In this setting, we propose trigonometric pruning which enables a significant decrease of the remaining search space for a query. Employing this new pruning method, we considerably speed up AkNN queries.

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Section: Related Workmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing All-Nearest-Neighbor Queries with Trigonometric Pruning

Emrich

Graf

Kriegel

et al. 2010

Lecture Notes in Computer Science

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show abstract

“…Moreover, queries involving more than one datasets are very frequent in real applications, and therefore, special attention has been given by the research community [11,5,6,14,17,3].…”

Section: Introductionmentioning

confidence: 99%

“…A clustering algorithm based on closest pairs has been proposed in [12]. In [2,3] the authors study applications of the k-NN join operation to knowledge discovery, which is a direct extension of the k-semi-closest-pair query. More specifically, the authors discuss the application of k-NN join to clustering, classification and sampling tasks in data mining operations, and they illustrate how these tasks can be performed more efficiently.…”

Section: Introductionmentioning

confidence: 99%

Closest Pair Queries with Spatial Constraints

Papadopoulos

Νανόπουλος

Manolopoulos

2005

Advances in Informatics

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Abstract. Given two datasets D A and D B the closest-pair query (CPQ) retrieves the pair (a,b), where a ∈ D A and b ∈ D B , having the smallest distance between all pairs of objects. An extension to this problem is to generate the k closest pairs of objects (k-CPQ). In several cases spatial constraints are applied, and object pairs that are retrieved must also satisfy these constraints. Although the application of spatial constraints seems natural towards a more focused search, only recently they have been studied for the CPQ problem with the restriction that D A = D B . In this work we focus on constrained closest-pair queries (CCPQ), between two distinct datasets D A and D B , where objects from DA must be enclosed by a spatial region R. A new algorithm is proposed, which is compared with a modified closest-pair algorithm. The experimental results demonstrate that the proposed approach is superior with respect to CPU and I/O costs.

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Seamlessly integrating similarity queries in SQL

et al. 2008

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Modern database applications are increasingly employing database management systems (DBMS) to store multimedia and other complex data. To adequately support the queries required to retrieve these kinds of data, the DBMS need to answer similarity queries. However, the standard structured query language (SQL) does not provide effective support for such queries. This paper proposes an extension to SQL that seamlessly integrates syntactical constructions to express similarity predicates to the existing SQL syntax and describes the implementation of a similarity retrieval engine that allows posing similarity queries using the language extension in a relational DBMS. The engine allows the evaluation of every aspect of the proposed extension, including the data definition language and data manipulation language statements, and employs metric access methods to accelerate the queries. Copyright © 2008 John Wiley & Sons, Ltd.

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The k-Nearest Neighbour Join: Turbo Charging the KDD Process

Cited by 126 publications

References 16 publications

Optimizing All-Nearest-Neighbor Queries with Trigonometric Pruning

Optimizing All-Nearest-Neighbor Queries with Trigonometric Pruning

Closest Pair Queries with Spatial Constraints

Seamlessly integrating similarity queries in SQL

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