Landscape of clustering algorithms

Jain, Anil K.; Topchy, A.; Law, Max W. K.; Buhmann, Joachim M.

doi:10.1109/icpr.2004.1334073

Cited by 71 publications

(39 citation statements)

References 9 publications

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“…Density-based clustering algorithms [4][5][6][7][8][9][10] circumvent this problem by using the number of data points in the neighborhood of a data point x i as a proxy for the data-point density. As a consequence, the objective function of these algorithms is often only given implicitly [11].…”

Section: Introductionmentioning

confidence: 99%

Common Nearest Neighbor Clustering—A Benchmark

Lemke

Keller

2018

Algorithms

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Cluster analyses are often conducted with the goal to characterize an underlying probability density, for which the data-point density serves as an estimate for this probability density. We here test and benchmark the common nearest neighbor (CNN) cluster algorithm. This algorithm assigns a spherical neighborhood R to each data point and estimates the data-point density between two data points as the number of data points N in the overlapping region of their neighborhoods (step 1). The main principle in the CNN cluster algorithm is cluster growing. This grows the clusters by sequentially adding data points and thereby effectively positions the border of the clusters along an iso-surface of the underlying probability density. This yields a strict partitioning with outliers, for which the cluster represents peaks in the underlying probability density-termed core sets (step 2). The removal of the outliers on the basis of a threshold criterion is optional (step 3). The benchmark datasets address a series of typical challenges, including datasets with a very high dimensional state space and datasets in which the cluster centroids are aligned along an underlying structure (Birch sets). The performance of the CNN algorithm is evaluated with respect to these challenges. The results indicate that the CNN cluster algorithm can be useful in a wide range of settings. Cluster algorithms are particularly important for the analysis of molecular dynamics (MD) simulations. We demonstrate how the CNN cluster results can be used as a discretization of the molecular state space for the construction of a core-set model of the MD improving the accuracy compared to conventional full-partitioning models. The software for the CNN clustering is available on GitHub.

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Section: Introductionmentioning

confidence: 99%

Common Nearest Neighbor Clustering—A Benchmark

Lemke

Keller

2018

Algorithms

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“…Besides, when applying Algorithm 1, we set nbitr = 10. We take the k-means algorithm as the baseline since this technique is pretty closed in spirit to our proposal (transfer operations) and since it was shown that it provides relevant results compared to other clustering techniques [25]. In our experiments, we set k as the number of true classes for the k-means algorithm while keeping free this parameter for Algorithm 1.…”

Section: Experiments Settingsmentioning

confidence: 99%

Cluster Analysis Based on the Central Tendency Deviation Principle

Ah-Pine

2009

Advanced Data Mining and Applications

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“…Zhang et al [86] compared different distance measures using spectral clustering and found that the more complex DTW and LCSS distances did not perform significantly better than Euclidean variants because the shape of their paths were simple. The clustering comparison by Jain et al [87] suggest there may only be five major classes of clustering techniques to try as others produce similar partitions. A complete comparison of distance/similarity measures, clustering methods, and validation schemes is paramount to furthering trajectory based analysis rather than focusing on the learning methodology.…”

Section: Clustering Performancementioning

confidence: 99%

A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance

Morris

Trivedi

2008

IEEE Trans. Circuits Syst. Video Technol.

421

224

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Abstract-This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.

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Landscape of clustering algorithms

Abstract: Abstract

Cited by 71 publications

References 9 publications

Common Nearest Neighbor Clustering—A Benchmark

Common Nearest Neighbor Clustering—A Benchmark

Cluster Analysis Based on the Central Tendency Deviation Principle

A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance

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