High-Dimensional Shared Nearest Neighbor Clustering Algorithm

Yin, Jian; Fan, Xian-Li; Chen, Yiqun; Ren, Jiangtao

doi:10.1007/11540007_60

Cited by 13 publications

(10 citation statements)

References 5 publications

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“…Redesigning metrics for high-dimensional data analysis might lead to certain improvements [3]. Secondary distances have shown promising results in practical applications, including the shared-neighbor distances [42,46,91], local scaling, NICDM, and global scaling (mutual proximity) [71].…”

Section: Distance Concentrationmentioning

confidence: 99%

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Clustering Evaluation in High-Dimensional Data

Tomašev

Radovanović

2016

Unsupervised Learning Algorithms

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Clustering evaluation plays an important role in unsupervised learning systems, as it is often necessary to automatically quantify the quality of generated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that do not estimate it internally. Many clustering quality indexes have been proposed over the years and different indexes are used in different contexts. There is no unifying protocol for clustering evaluation, so it is often unclear which quality index to use in which case. In this chapter, we review the existing clustering quality measures and evaluate them in the challenging context of high-dimensional data clustering. High-dimensional data is sparse and distances tend to concentrate, possibly affecting the applicability of various clustering quality indexes. We analyze the stability and discriminative power of a set of standard clustering quality measures with increasing data dimensionality. Our evaluation shows that the curse of dimensionality affects different clustering quality indexes in different ways and that some are to be preferred when determining clustering quality in many dimensions.

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Section: Distance Concentrationmentioning

confidence: 99%

“…Common alternatives to subspace clustering include approximate expectation maximization (EM) [23], spectral clustering [89], shared-neighbor methods [46,91,93] and relevant set correlation [41,88], and clustering ensembles [31,32].…”

Section: Clustering Techniques For High-dimensional Datamentioning

confidence: 99%

Clustering Evaluation in High-Dimensional Data

Tomašev

Radovanović

2016

Unsupervised Learning Algorithms

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“…Another popular approach to high-dimensional clustering is based on sharedneighbor similarity measures, that reduce the negative effects of the dimensionality curse [22,34,47,50,82,86]. Cardinality of the intersection between k-nearest neighbor sets can be used for measuring pairwise similarity between points and these secondary distances have been shown to be less susceptible to concentration and usually exhibit more favourable properties than the original metrics [33].…”

Section: High-dimensional Data Clusteringmentioning

confidence: 99%

Hubness-Based Clustering of High-Dimensional Data

Tomašev

Radovanović

Mladenić

et al. 2014

Partitional Clustering Algorithms

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Hubness has recently been established as a significant property of k-nearest neighbor (k-NN) graphs obtained from high-dimensional data using a distance measure, with traits and effects relevant to the cluster structure of data, as well as clustering algorithms. The hubness property is manifested with increasing (intrinsic) data dimensionality. The distribution of data point in-degrees, i.e. the number of times points appear among the k nearest neighbors of other points in the data, becomes highly skewed. This results in hub points that can have in-degrees multiple orders of magnitude higher than expected. In this chapter we review and refine existing work which explains the mechanisms of the phenomenon, establishes the location of hub points near central regions of clusters in the data, and shows how hubness can negatively affect existing clustering algorithms by virtue of hub points lowering between-cluster distance. Next, we review the newly proposed partitional clustering algorithms, based on K-means, which take advantage of hubness by employing hubs in the process of cluster prototype selection. These "soft" K-means extensions avoid premature convergence to suboptimal stable cluster configurations and are able to reach the global optima more often. The algorithms offer significant improvements over the K-means baseline in scenarios involving high-dimensional and noisy data. The improvements stem from a better placement of hub points into clusters, which helps in increasing the between-cluster distance. Finally, we introduce novel clustering algorithms as "kernelized" versions of the most successful hubness-based methods discussed above, that are able to more effectively handle arbitrarily-shaped clusters.

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“…In this approach, two elements are grouped if they share many of their nearest neighbors, and if they are themselves nearest neighbors of the other element. This algorithm has been reused in many domains such as information retrieval [30], databases [31] and recently in data traffic [32].…”

Section: Clusteringmentioning

confidence: 99%

From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

Brun

Castagnos

Boyer

2011

Advances in Multimedia

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The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors.

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High-Dimensional Shared Nearest Neighbor Clustering Algorithm

Cited by 13 publications

References 5 publications

Clustering Evaluation in High-Dimensional Data

Clustering Evaluation in High-Dimensional Data

Hubness-Based Clustering of High-Dimensional Data

From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

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