PBIRCH: A Scalable Parallel Clustering algorithm for Incremental Data

Garg, Ashwani; Mangla, Ashish; Gupta, Neelima; Bhatnagar, Vasudha

doi:10.1109/ideas.2006.36

Cited by 41 publications

(24 citation statements)

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“…The PBIRCH algorithm proposed in [9] uses message-passing to distribute data to the processors and also to exchange the information required to update the clusters. In [17], a parallel bisecting k-means with prediction algorithm is proposed, and its performance is compared with that of a parallel k-means algorithm on a cluster of computers.…”

Section: Related Workmentioning

confidence: 99%

A parallel text document clustering algorithm based on neighbors

Luo

Chung

2015

Cluster Comput

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In this paper, we propose a new parallel algorithm for text document clustering based on the concept of neighbor (Guha et al. in Inf Syst 25(5):345-366, 2000). If two documents are similar enough, they are considered as neighbors of each other. The new algorithm is named parallel k-means based on neighbors (PKBN), and it is a parallel version of sequential k-means based on neighbors (SKBN) that we proposed in Luo et al. (Data Knowl Eng 68(11):1271-1288, 2009). PKBN fully exploits the data-parallelism of SKBN and adopts a new parallel pairgenerating method to build the neighbor matrix. Our new parallel pair-generating method causes less communication overhead between processors than existing methods. PKBN is designed for message-passing multiprocessor systems and is implemented on a cluster of Linux workstations to analyze its performance. Our experimental results on real-life data sets demonstrate that PKBN is very efficient and has good scalability with respect to the number of processors and the size of data set.

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Section: Related Workmentioning

confidence: 99%

A parallel text document clustering algorithm based on neighbors

Luo

Chung

2015

Cluster Comput

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“…Different elaborated taxonomies of existing clustering algorithms are given in the literature. Many parallel clustering versions based on these algorithms have been proposed in the literature [17], [27], [28], [29], [30], [31], [32]. These algorithms are further classified into two sub-categories.…”

Section: Introductionmentioning

confidence: 99%

Efficient Large Scale Clustering Based on Data Partitioning

Bendechache

Kechadi

Le-Khac

2016

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Abstract-Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input parameters. Distributed clustering techniques constitute a very good alternative to the big data challenges (e.g.,Volume, Variety, Veracity, and Velocity). Usually these techniques consist of two phases. The first phase generates local models or patterns and the second one tends to aggregate the local results to obtain global models. While the first phase can be executed in parallel on each site and, therefore, efficient, the aggregation phase is complex, time consuming and may produce incorrect and ambiguous global clusters and therefore incorrect models.In this paper we propose a new distributed clustering approach to deal efficiently with both phases; generation of local results and generation of global models by aggregation. For the first phase, our approach is capable of analysing the datasets located in each site using different clustering techniques. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. For the evaluation, we use two well-known clustering algorithms; K-Means and DBSCAN. One of the key outputs of this distributed clustering technique is that the number of global clusters is dynamic; no need to be fixed in advance. Experimental results show that the approach is scalable and produces high quality results.

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“…Co-clustering [32] endeavor at clustering both the samples and the characteristics concurrently to recognize hidden impede organization implanted into the data matrix. Clustering ensembles have materialized as a high-flying procedure for advancing robustness, steadiness and accuracy of unsupervised classification solutions [102]. The concurrency of clustering algorithms is foreordained, implemented and applied to numerous applications.…”

Section: Data Clusteringmentioning

confidence: 99%