BANG-Clustering: A novel grid-clustering algorithm for huge data sets

Schikuta, Erich; Erhart, Martin

doi:10.1007/bfb0033313

Cited by 6 publications

(2 citation statements)

References 3 publications

(1 reference statement)

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“…Agglomerative (Jain & Dubes, 1988) ✓ ✓ BANG (Schikuta & Erhart, 1998) ✓ BIRCH (Zhang, Ramakrishnan, & Livny, 1996) ✓ BSAS (Theodoridis & Koutroumbas, 2009) ✓ ✓ CLARANS (Ng & Han, 2002) ✓ CLIQUE (Agrawal, Gunopulos, & Raghavan, 2005) ✓ ✓ CURE (Guha, Rastogi, & Shim, 1998) ✓ ✓ DBSCAN (Ester, Kriegel, Sander, & Xu, 1996) ✓ ✓ Elbow (Thorndike, 1953) ✓ ✓ EMA (Gupta & Chen, 2011) ✓ GA -Genetic Algorithm (Cowgill, Harvey, & Watson, 1999) ✓ ✓…”

Section: Algorithmmentioning

confidence: 99%

PyClustering: Data Mining Library

Новиков¹

2019

JOSS

105

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Section: Algorithmmentioning

confidence: 99%

PyClustering: Data Mining Library

Новиков¹

2019

JOSS

105

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show abstract

“…The new researches are focusing on such aspects as Projective Clustering [3], Hierarchical Clustering [4], and Feature weighting [5]. Grid partition method has the merit of dataset reduction [6][7][8][9][10] and has always been employed to improve the clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Fast K-Means Clustering Algorithm Based on Grid Data Reduction

Shen

Chen

2008

2008 IEEE Aerospace Conference

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K-Means is a popular clustering algorithm to find the clustering easily by iteration. But the computational complexity of the traditional K-Means due to accessing the whole data in each cycle of iterative operations is too great to make it fit for very large data set. This paper presents a new clustering algorithm we have developed, Fast K-Means Clustering Algorithm based on Grid Data Reduction (GDR-FKM), by which clustering operations can be quickly performed on very large data set. Application of the algorithm to analysis of the data relativity in TT&C has demonstrated its celerity and accuracy.

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Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer

Özyer

Alhajj

2008

Appl Intell

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This paper applies divide and conquer approach in an iterative way to handle the clustering process. The target is a parallelized effective and efficient approach that produces the intended clustering result. We achieve scalability by first partitioning a large dataset into subsets of manageable sizes based on the specifications of the machine to be used in the clustering process; then cluster the partitions separately in parallel. The centroid of each obtained cluster is treated like the root of a tree with instances in its cluster as leaves. The partitioning and clustering process is iteratively applied on the centroids with the trees growing up until we get the final clustering; the outcome is a forest with one tree per cluster. Finally, a conquer process is performed to get the actual intended clustering, where each instance (leaf node) belongs to the final cluster represented by the root of its tree. We use multi-objective genetic algorithm combined with validity indices to decide on the number of classes. This approach fits well for interactive online clustering. It facilitates for incremental clustering because chunks of instances are clustered as stand alone sets, and then the results are merged with existing clusters. This is attractive and feasible because we consider the clustering of only centroids after the first clustering stage. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

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BANG-Clustering: A novel grid-clustering algorithm for huge data sets

Cited by 6 publications

References 3 publications

PyClustering: Data Mining Library

PyClustering: Data Mining Library

A Fast K-Means Clustering Algorithm Based on Grid Data Reduction

Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer

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