scholarly journals A Local Clustering Algorithm for Connection Graphs

2013 ◽  
pp. 26-43 ◽  
Author(s):  
Fan Chung ◽  
Mark Kempton
Keyword(s):  
Clustering Algorithm ◽  
Local Clustering

Hybrid Partitioning-Density Algorithm for K-Means Clustering of Distributed Data Utilizing OPTICS

2019 ◽  
Vol 15 (4) ◽  
pp. 1-20
Author(s):  
Mikołaj Markiewicz ◽  
Jakub Koperwas
Keyword(s):  
Clustering Algorithm ◽  
Additional Data ◽  
Processing Unit ◽  
Distributed Data ◽  
Local Models ◽  
Excellent Outcome ◽  
Central Processing ◽  
Clustering Model ◽  
Local Clustering ◽  
Fragmented Data

The authors present the first clustering algorithm for use with distributed data that is fast, reliable, and does not make any presumptions in terms of data distribution. The authors' algorithm constructs a global clustering model using small local models received from local clustering statistics. This approach outperforms the classical non-distributed approaches since it does not require downloading all of the data to the central processing unit. The authors' solution is a hybrid algorithm that uses the best partitioning and density-based approach. The proposed algorithm handles uneven data dispersion without a transfer overload of additional data. Experiments were carried out with large datasets and these showed that the proposed solution introduces no loss of quality compared to non-distributed approaches and can achieve even better results, approaching reference clustering. This is an excellent outcome, considering that the algorithm can only build a model from fragmented data where the communication cost between nodes is negligible.

scholarly journals A Local Clustering Algorithm for Connection Graphs

2014 ◽  
Vol 11 (4-5) ◽  
pp. 333-351
Author(s):  
Fan Chung ◽  
Mark Kempton
Keyword(s):  
Clustering Algorithm ◽  
Local Clustering

scholarly journals A Special Local Clustering Algorithm for Identifying the Genes Associated With Alzheimer's Disease

2010 ◽  
Vol 9 (1) ◽  
pp. 44-50 ◽  
Author(s):  
Chao-Yang Pang ◽  
Wei Hu ◽  
Ben-Qiong Hu ◽  
Ying Shi ◽  
C.R. Vanderburg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clustering Algorithm ◽  
Local Clustering

scholarly journals Local clustering via approximate heat kernel PageRank with subgraph sampling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhenqi Lu ◽  
Johan Wahlström ◽  
Arye Nehorai
Keyword(s):  
Heat Kernel ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Graph Clustering ◽  
Approximate Algorithms ◽  
Local Cluster ◽  
Massive Graphs ◽  
Approximation Errors ◽  
Local Clustering ◽  
Large Systems

AbstractGraph clustering, a fundamental technique in network science for understanding structures in complex systems, presents inherent problems. Though studied extensively in the literature, graph clustering in large systems remains particularly challenging because massive graphs incur a prohibitively large computational load. The heat kernel PageRank provides a quantitative ranking of nodes, and a local cluster can be efficiently found by performing a sweep over the heat kernel PageRank vector. But computing an exact heat kernel PageRank vector may be expensive, and approximate algorithms are often used instead. Most approximate algorithms compute the heat kernel PageRank vector on the whole graph, and thus are dependent on global structures. In this paper, we present an algorithm for approximating the heat kernel PageRank on a local subgraph. Moreover, we show that the number of computations required by the proposed algorithm is sublinear in terms of the expected size of the local cluster of interest, and that it provides a good approximation of the heat kernel PageRank, with approximation errors bounded by a probabilistic guarantee. Numerical experiments verify that the local clustering algorithm using our approximate heat kernel PageRank achieves state-of-the-art performance.

A Variational Level Set Model Based on Local Clustering for Image Segmentation

2014 ◽  
Vol 556-562 ◽  
pp. 4797-4801
Author(s):  
Yu Zhou ◽  
Wei Guo Zhang ◽  
Li Feng Li
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Criterion Function ◽  
Model Based ◽  
Local Clustering ◽  
Variational Level Set ◽  
Local Intensity ◽  
Clustering Criterion ◽  
Level Set Model

For images with intensity inhomogeneities that can’t get accurate segmentation results, this paper proposes a variational level set model based on local clustering. First,based on the model of images with intensity inhomogeneities, we use the K-mean clustering algorithm for intensity clustering in a neighborhood of each point of images with intensity inhomogeneities, and define a local clustering criterion function for the image intensities in the neighborhood. Then this local clustering criterion function is then integrated with respect to the neighborhood center to give a global criterion of image segmentation. This criterion defines an energy function as a local intensity fitting term in the level set model. By minimizing this energy, our method is able to get the accurate image segmentation. The image segmentation results prove that our model in the aspect of segmenting images with intensity inhomogeneity is better than piecewise constant (PC) models, and the segmentation efficiency is higher than region-scalable fitting (RSF) model.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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