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Sander, Jöerg; Ester, Martin; Kriegel, Hans‐Peter; Xu, Xiaowei

doi:10.1023/a:1009745219419

Cited by 1,162 publications

(211 citation statements)

References 23 publications

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“…The four main techniques used in spatial data mining are spatial clustering [7], spatial classification [8], the spatial association rule [9], and spatial characterization [10]. Spatial clustering is a technique used to classify data with similar geographic and locational characteristics into the same group.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an efficient grid-based k-prototypes algorithm, GK-prototypes, which achieves high performance for clustering spatial objects. The first proposed algorithm utilizes both maximum and minimum distance between cluster centers and a cell, which can reduce unnecessary distance calculation. The second proposed algorithm as an extension of the first proposed algorithm, utilizes spatial dependence; spatial data tends to be similar to objects that are close. Each cell has a bitmap index which stores the categorical values of all objects within the same cell for each attribute. This bitmap index can improve performance if the categorical data is skewed. Experimental results show that the proposed algorithms can achieve better performance than the existing pruning techniques of the k-prototypes algorithm.

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Section: Introductionmentioning

confidence: 99%

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Jang

Kim

et al. 2018

Sustainability

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“…It requires two parameters: distance (radius of the detection region) and the minimum sample points (minPts). DBSCAN can be used with any distance function [2] [10] (as well as similarity functions or other predicates) [9].…”

Section: Dbscan Algorithmmentioning

confidence: 99%

Time Series Clustering Based on Singularity

Chang

Ding

2017

INT J COMPUT COMMUN

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With relevant theories on time series clustering, the thesis makes research into similarity clustering process of time series from the perspective of singularity and proposes the time series clustering based on singularity applying K-means and DBScan clustering algorithms according to the shortage of traditional clustering algorithm. In accordance with the general clustering process of time series, time series clustering based on singularity and K-means are made respectively to get different clustering results and make a comparison, thus proving that similarity clustering research of time series from the perspective of singularity can better find out people's concern on time series.

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“…In [21] a method called k-distance diagram is proposed to determine a suitable radius ε. For this purpose, a number of objects (typically 5-20 percent of the database) is randomly selected.…”

Section: K-distance Diagramsmentioning

confidence: 99%

Supporting KDD Applications by the k-Nearest Neighbor Join

Böhm¹,

Krebs²

2003

Lecture Notes in Computer Science

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Abstract. The similarity join has become an important database primitive to support similarity search and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Well-known are two types of the similarity join, the distance range join where the user defines a distance threshold for the join, and the closest point query or k-distance join which retrieves the k most similar pairs. In this paper, we propose an important, third similarity join operation called k-nearest neighbor join which combines each point of one point set with its k nearest neighbors in the other set. We discover that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbor classification, data cleansing, postprocessing of sampling-based data mining etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. Our list of possible applications includes standard methods for all stages of the KDD process including preprocessing, data mining, and postprocessing. Thus, our method is turbo charging the complete KDD process.

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Untitled

Cited by 1,162 publications

References 23 publications

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

An Efficient Grid-Based K-Prototypes Algorithm for Sustainable Decision-Making on Spatial Objects

Time Series Clustering Based on Singularity

Supporting KDD Applications by the k-Nearest Neighbor Join

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