A Density-based Clustering Algorithm Using Adaptive Parameter K-Reverse Nearest Neighbor

Pei, Pengyu; Zhang, Dong; Guo, Feng

doi:10.1109/icpics47731.2019.8942511

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…K-nearest neighbor query [41][42][43][44] based on the 3D multilevel adaptive grid and R+ tree can make use of the advantages of the index structure, combined with the idea of space division, and query the result set according to certain rules. Operations of the intermediate nodes of the lattice-R+ tree is pruned to obtain a preliminary candidate set, and then we perform a refinement operation to obtain the final result set.…”

Section: B K-nearest Neighbor Querymentioning

confidence: 99%

Research on Hybrid Index Based on 3D Multi-Level Adaptive Grid and R+ Tree

et al. 2021

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With the widespread application of Geographic Information System (GIS), three-dimensional spatial data, as the reflection of the real world entity, has an increasing amount of data, and the phenomenon of uneven data distribution appears. If a single spatial index structure is used to store and manage these data, there will be a waste of storage space and low query efficiency. A hybrid index structure based on 3D multi-level adaptive grid and R+ tree was proposed to solve these problems. The index structure was mainly composed of two structures, multi-level grid and R+ tree. Firstly, the data set was processed by the multi-level automatic grid algorithm based on normal distribution, and the length, width and height of the grid were obtained. Secondly, a multi-level adaptive grid structure was used to partition the data space quickly and effectively, and the advantage of zero overlap of the intermediate nodes of the R+ tree was used for efficient indexing. Finally, the maintenance and query algorithms of the index structure were given in detail, which solved the problem of low index establishment and retrieval efficiency under the condition of uneven distribution of massive data sets. In this paper, a data set subject to Gauss distribution was used to simulate the distribution of three-dimensional data. Through a large number of experimental comparison tests, it was proved that the hybrid index structure based on 3D multi-level adaptive grid-R+ tree proposed in this paper had good performance in both index structure construction and query in the case of massive data sets or uneven data distribution.

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Section: B K-nearest Neighbor Querymentioning

confidence: 99%

Research on Hybrid Index Based on 3D Multi-Level Adaptive Grid and R+ Tree

et al. 2021

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“…The algorithms mentioned above are all for Euclidean space and road network space, and they are not completely applicable for group reverse farthest neighbor query in obstacle space. A new density-based clustering algorithm, RNN-DBSCAN, was presented which uses reverse nearest neighbor counts as an estimate of observation density by Pei et al [34]. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations.…”

Section: Related Work a The Rearch Of Nearest And Farthest Neighmentioning

confidence: 99%

A Voronoi-Based Group Reverse k Farthest Neighbor Query Method in the Obstacle Space

et al. 2020

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With the rapid development of intelligent transportation and geographic information system, spatial data query technology has attracted the attention of many scholars. Among them, the reverse farthest neighbor query from the data point to find the target point as its farthest adjacent data points, used to obtain target set of weak influence. Its research results have been widely applied to facility location, earthquake relief, marketing and other major areas. Thus, the research of reverse furthest neighbor query technology is of great significance. However, the existing methods only deal with a single query point, and do not consider how to obtain the optimal farthest neighbor position of group reverse when the number of query points changes from one to a group. In addition, considering it is difficult to avoid some geographical location restrictions in the real situation, the existing studies are limited to road network or Euclidean space simplely, without taking the existence of obstacles into consideration. To this end, this paper proposed the V-OGRkFN(Voronoi-Obstacle Group Reverse k Farthest Neighbor) algorithm. Firstly, the algorithm gets the minimum cover circle of query points which are considered into a whole. Secondly, we use the framework based on the filtering and refining process of query by pruning strategy based on Voronoi diagram's properties. Then we get the candidate set using the theorem of transformation between k nearest neighbors and k farthest neighbors. Finally, the refining algorithm is given to get the final results. V-OGRkFN algorithm shows great performance of reverse k farthest neighbor query through the specific comparative experimental analysis. INDEX TERMS Obstacle space, reverse farthest neighbor query, Voronoi diagram, minimum coverage circle.

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“…There are several clustering algorithms based on data stream such as [9], [10], [11], [12], [13], [14], [15], [16], [17], [18] discussed in this mentioned paper in detail. Among them density-based clustering algorithm [19] gives insight on clusters which are of arbitrary shape and aiding outliers detection tools.…”

Section: Introductionmentioning

confidence: 99%

Improving Performance Parameters of Clusters Using Density-Based Algorithm

Keshvani

Shukla

Jayswal

et al. 2023

ACE

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With the advancement in technology, data generated by non-stationary in day-to-day life is massive, continuous and rapid. Many applications such as IoT, transaction systems, network sensors, video surveillance systems, and network intrusion detection systems generate a massive amount of real-time data. The data used in traditional data mining is static in nature, and it can be revised for processing and Analysis. While data in data stream mining is dynamic in nature and it never stops. Besides, the data generated may have a change imbibed in its characteristics over a long/short period of time which is called concept drift. So, analysing such data has huge inbuilt challenges that deal with the dynamism of the characteristics of data itself. This dynamic nature is because of fast and continuous changing data and its enormity. To overcome this limitation, we can use modified clustering techniques that could help us in proper data analysis. Clustering is an effective method used in data mining; but clustering data streams may add some additional challenges such as storage capacity, limited time, one pass and rate of arrival. Furthermore, data streams are fickle in nature and because of this behaviour it needs to be processed as and when it arrives. In addition to that, knowledge about the number of clusters like in K-means clustering is unknown. In view of these characteristics of the data stream, the information or the data generated in the data stream are non-deterministic. Such non-deterministic information contains noise points or outliers, so developing an effective clustering algorithm in a data stream is a crucial task. These methods can work with labelled data in data stream clustering, which has the potential to identify clusters of any shape and noise. The motivation for this research work is using the said algorithm to address and overcome the constraints of the data stream and to dig out the best knowledge from it.

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A Density-based Clustering Algorithm Using Adaptive Parameter K-Reverse Nearest Neighbor

Cited by 7 publications

References 7 publications

Research on Hybrid Index Based on 3D Multi-Level Adaptive Grid and R+ Tree

Research on Hybrid Index Based on 3D Multi-Level Adaptive Grid and R+ Tree

A Voronoi-Based Group Reverse k Farthest Neighbor Query Method in the Obstacle Space

Improving Performance Parameters of Clusters Using Density-Based Algorithm

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