A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method

Kumar, Kanike Vinod; Reddy, A. Rama Mohan

doi:10.1016/j.patcog.2016.03.008

Cited by 242 publications

(41 citation statements)

References 35 publications

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“…Researches have been conducted to find spatial co-location patterns in spaces using Euclidean distance [29] and road network distance [30]. Spatial clustering is generally classified into partitioning based algorithms [31,32], density based algorithms [33,34], and grid-based algorithms (see Section 2.3). Among the partitioning based methods, the best known algorithm is a k-means [31] algorithm that classifies a dataset into k groups.…”

Section: Spatial Data Miningmentioning

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: Spatial Data Miningmentioning

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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“…As denoted in Table 2, the proposed approach is almost superior to other methods in terms of accuracy. The proposed approach is even superior as compared with DBSCAN algorithm (Kumar & Reddy, 2016), which shows its appropriate performance. A main drawback of DBSCAN algorithm is the tuning of its parameters which is not an issue in the proposed approach.…”

Section: Resultsmentioning

confidence: 92%

“…Guruacharya, Niyato, Bennis, and Kim (2013) used a new coalitional game model for data clustering. Kumar and Reddy (2016) pointed out the well-known density-based spatial clustering of applications with noise (DBSCAN) method for density-based and shape-independent clustering. Rota Bulo and Pelillo addressed a hypergraph clustering problem that extracts groups with maximum dependency (Rota Bulo & Pelillo, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…The superiority of the clustering methods is usually evaluated in terms of speed, hardware and software limitations, and also simplicity and the complexity of the calculations. In this study, precision of the method is considered and therefore, the proposed GCGC method is compared with traditional methods such as game-based clustering (Li et al, 2010), Kmeans, PCA_Kmeans and DBSCAN (Kumar & Reddy, 2016) in terms of the purity index as denoted in Equation 8.…”

Section: Evaluation Criteriamentioning

confidence: 99%

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A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

2018

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Different clustering approaches consider different aspects of quality including accuracy of cluster formation, speed of execution, and resource requirements. A major issue in data clustering is to consider the density of clusters and their structure. A challenge is to relax any assumption on the clusters shape such as sphericity, which is common in most partitioning approaches such as K‐means. In this article, with the help of the coalitional game theory and the concept of geodesic distance calculation, a density‐based shape‐independent clustering approach is proposed. In addition to the emphasis on the application of game theory, we also pay attention to the relative neighbourhood of data, which is depicted using geodesic distance. Geodesic distance is a well‐known measure for finding the manifold of data. The new idea supposes to discover any embedded structure of data and avoid finding only spherical clusters. The proposed approach is evaluated on a number of standard University of California, Irvine (UCI) datasets, and the results show the effectiveness of the proposed approach in comparison with some other approaches.

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“…The similarity feature or measure of the objects with respect to each other is generally distance or density function. The traditional clustering techniques, methods, and algorithms are widely studied in the research community [7][8][9][10][11][12]. Recently, clustering with data streams and problems related to this are also discussed.…”

Section: Introductionmentioning

confidence: 99%

Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion

Kokate¹,

Deshpande²,

Mahalle³

et al. 2018

BDCC

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Data growth in today’s world is exponential, many applications generate huge amount of data streams at very high speed such as smart grids, sensor networks, video surveillance, financial systems, medical science data, web click streams, network data, etc. In the case of traditional data mining, the data set is generally static in nature and available many times for processing and analysis. However, data stream mining has to satisfy constraints related to real-time response, bounded and limited memory, single-pass, and concept-drift detection. The main problem is identifying the hidden pattern and knowledge for understanding the context for identifying trends from continuous data streams. In this paper, various data stream methods and algorithms are reviewed and evaluated on standard synthetic data streams and real-life data streams. Density-micro clustering and density-grid-based clustering algorithms are discussed and comparative analysis in terms of various internal and external clustering evaluation methods is performed. It was observed that a single algorithm cannot satisfy all the performance measures. The performance of these data stream clustering algorithms is domain-specific and requires many parameters for density and noise thresholds.

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A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method

Cited by 242 publications

References 35 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

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

Data Stream Clustering Techniques, Applications, and Models: Comparative Analysis and Discussion

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