Decentralized Probabilistic Text Clustering

Papapetrou, Odysseas; Siberski, Wolf; Fuhr, Norbert

doi:10.1109/tkde.2011.120

Cited by 12 publications

(9 citation statements)

References 25 publications

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“…Researchers focus on solving this battery grouping by data clustering algorithms [15]. As an important form of data analysis technology, data clustering [16] has been applied to many areas, such as data mining [17], content retrieval [18,19] and image segmentation [20]. By clustering algorithms, data can be divided into different clusters based on some criteria, and data in the same cluster are similar to each other and dissimilar otherwise as a result.…”

Section: Related Workmentioning

confidence: 99%

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Yang

Gao

et al. 2017

Energies

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Abstract:The inconsistent characteristics of individual power batteries in a battery pack can seriously affect the performance and service life of the whole pack. Battery grouping is an effective approach for dealing with the inconsistency problem by grouping batteries with similar characteristics in the same battery pack. In actual production, the battery grouping process still relies on the traditional manual method, which results in high labor and time costs. In this paper, a robust and effective battery grouping method based on the characteristic distribution model is developed. Specifically, a novel characteristic distribution model is proposed to determine the grouping priority of different batteries. Then, an improved k-nearest-neighbor algorithm is used to decide which batteries should be group into the same battery pack. Experimental results demonstrate the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

Yang

Gao

et al. 2017

Energies

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“…In addition to the above two kinds of clustering methods, probability-based clustering method has caused the great concern of many researchers in recent years [12][13][14][15][16][17][18][19][20][21]. Probability-based clustering method mainly includes model-based strategy [12][13][14][15][16] and evolutionary probabilistic modeling strategy [17][18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

Fan

2015

Neural Comput & Applic

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The Survival of the Fittest is a principle which selects the superior and eliminates the inferior in the nature. This principle has been used in many fields, especially in optimization problem-solving. Clustering in data mining community endeavors to discover unknown representations or patterns hidden in datasets. Hierarchical clustering algorithm (HCA) is a method of cluster analysis which searches the optimal distribution of clusters by a hierarchical structure. Strategies for hierarchical clustering generally have two types: agglomerative with a bottom-up procedure and divisive with a top-down procedure. However, most of the clustering approaches have two disadvantages: the use of distance-based measurement and the difficulty of the clusters integration. In this paper, we propose an optimal probabilistic estimation (OPE) approach by exploiting the Survival of the Fittest principle. We devise a hierarchical clustering algorithm (HCA) based on OPE, also called OPE-HCA. The OPE-HCA combines optimization with probability and agglomerative HCA. Experimental results show that the OPE-HCA has the ability of searching and discovering patterns at different description levels and can also obtain better performance than many clustering algorithms according to NMI and clustering accuracy measures.

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“…The authors determined the clustering quality based on the skewness of similarity histograms of the individual cluster which deteriorates with increase in hierarchy. Decentralized Probabilistic Text Clustering for peerpeer networks is proposed by Papapetrou et al (2012). This work uses a probabilistic approach using Distributed Hash Table (DHT) for assigning documents to clusters which increases the scalability algorithm but there is a decrease in speed-up with increase in dataset.…”

Section: Distributed Document Clusteringmentioning

confidence: 99%

An efficient hybrid distributed document clustering algorithm

Judith¹,

Jayakumari²

2015

Sci. Res. Essays

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Recent advances in information technology have led to an increase in volumes of data thereby exceeding beyond petabytes. Clustering distributed document sets from a central location is difficult due to the massive demand of computational resources. So there is a need for distributed document clustering algorithms to cluster documents using distributed resources. The greatest challenge in this area of distributed document clustering is the clustering quality and speedup associated with increase in document sets. The proposed clustering algorithm uses a hybrid algorithm which comprises of Particle Swarm Optimization (PSO), K-Means clustering and Latent Semantic Indexing (LSI) algorithm (PKMeansLSI), and uses MapReduce framework for distributed computation. The resultant of this is that it ultimately promotes clustering quality of the algorithm. The MapReduce framework and its corresponding implementation Hadoop is used as a distributed programming model which stresses on the improvement factor of the speedup of algorithm. The execution time is dramatically reduced as the dimensionality of documents is reduced. Experiment results show improved quality and effectiveness of the hybrid algorithm with varying increase in document size.

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Decentralized Probabilistic Text Clustering

Cited by 12 publications

References 25 publications

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

A Robust Battery Grouping Method Based on a Characteristic Distribution Model

OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

An efficient hybrid distributed document clustering algorithm

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