Grid-clustering: an efficient hierarchical clustering method for very large data sets

Schikuta, Erich

doi:10.1109/icpr.1996.546732

Cited by 126 publications

(64 citation statements)

References 6 publications

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“…It gives birth either to a buddy region or to a newly enclosed region. Further details on splitting strategies can be found in [34]. The iterative halving strongly differs from the R + -tree splitting strategy.…”

Section: Bang Directorymentioning

confidence: 99%

“…BANG-clustering is a grid clustering approach that relies on a main memory kd-tree accommodated from the BANG file. It is a direct descendant from GRIDCLUS [34], itself based on the Grid file.…”

Section: Density-based Clusteringmentioning

confidence: 99%

“…Neighborhood is defined as a shared (n − 1)-dimensional hyperplane between two block regions. The algorithm is detailed in [34].…”

Section: Density-based Clusteringmentioning

confidence: 99%

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BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

Anjum

Raschia

2017

Computers

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Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions.

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Section: Bang Directorymentioning

confidence: 99%

Section: Density-based Clusteringmentioning

confidence: 99%

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BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

Anjum

Raschia

2017

Computers

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“…The MDG divides the feature space into hyper-rectangular blocks so that it organizes the feature space surrounding the patterns and not the patterns themselves (E. Schikuta ,1996). In (A. Amini and T. Wah, 2011) grid clustering is used as a natural choice for infinite data streams which are mapped to finite grid cells where the synopsis information for data streams is contained in the grid cells.…”

Section: Related Workmentioning

confidence: 99%

Nonlinearity Reduction of Manifolds using Gaussian Blur for Handshape Recognition based on Multi-Dimensional Grids

Farouk¹,

Sutherland²,

Shokry³

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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Abstract:This paper presents a hand-shape recognition algorithm based on using multi-dimensional grids (MDGs) to divide the feature space of a set of hand images. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method to generate eigenspaces from example images. Images are blurred by convolving with a Gaussian kernel as a low pass filter. Image blurring is used to reduce the non-linearity in the manifolds within the eigenspaces where MDG structure can be used to divide the spaces linearly. The algorithm is invariant to linear transformations like rotation and translation. Computer generated images for different hand-shapes in Irish Sign Language are used in testing. Experimental results show accuracy and performance of the proposed algorithm in terms of blurring level and MDG size.

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“…, CLARA [17], CLARANS [23], Focusing Techniques [5], P-CLUSTER [14], DBSCAN [4], Ejcluster [11], BIRCH [27], GRIDCLUS [26]. 이런 군집화 방법들을 분 류하면 계층적 기법 (hierarchical algorithms)과 분할 기법 (partitioning algorithms)으로 나눌 수 있다 [12].…”

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Fast K-Means Clustering Algorithm using Prediction Data

Jee¹,

Lee²,

Lee³

2009

The Journal of the Korea Contents Association

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In this paper we proposed a fast method for a K-Means Clustering algorithm. The main characteristic of this method is that it uses precalculated data which possibility of change is high in order to speed up the algorithm. When calculating distance to cluster centre at each stage to assign nearest prototype in the clustering algorithm, it could reduce overall computation time by selecting only those data with possibility of change in cluster is high.Calculation time is reduced by using the distance information produced by K-Means algorithm when computing expected input data whose cluster may change, and by using such distance information the algorithm could be less affected by the number of dimensions. The proposed method was compared with original K-Means method -Lloyd's and the improved method KMHybrid. We show that our proposed method significantly outperforms in computation speed than Lloyd's and KMHybrid when using large size data which has large amount of data, great many dimensions and large number of clusters.■ keyword :|Pattern Recognition|Data Mining|K-Means Clustering|

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Grid-clustering: an efficient hierarchical clustering method for very large data sets

Cited by 126 publications

References 6 publications

BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication

Nonlinearity Reduction of Manifolds using Gaussian Blur for Handshape Recognition based on Multi-Dimensional Grids

Fast K-Means Clustering Algorithm using Prediction Data

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