Complex Network Clustering Algorithms

Yang, Bo; Liu, Dayou; Liu, Jiming; Jin, Di; Ma, Huadóng

doi:10.3724/sp.j.1001.2009.00054

Cited by 60 publications

(23 citation statements)

References 33 publications

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“…Currently, a number of methods have been proposed in order to detect protein functional modules and predict protein functions from PPI networks, such as Markov random field method and spectral clustering method [2]. However, PPI networks are complex because of enormous data volume.…”

Section: Introductionmentioning

confidence: 99%

ABC and IFC: Modules Detection Method for PPI Network

Lei

Tian

et al. 2014

BioMed Research International

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Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership matrix. Then the cluster centers are updated through different functions of bees in ABC algorithm; then the clustering result is obtained through IFC method based on the new optimized cluster center. To illustrate its performance, the ABC-IFC method is compared with the traditional fuzzy C-means clustering and IFC method. The experimental results on MIPS dataset show that the proposed ABC-IFC method not only gets improved in terms of several commonly used evaluation criteria such as precision, recall, and P value, but also obtains a better clustering result.

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Section: Introductionmentioning

confidence: 99%

ABC and IFC: Modules Detection Method for PPI Network

Lei

Tian

et al. 2014

BioMed Research International

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“…For example, the actual partitions of some social networks correspond to locally maximum modularity values rather than global optima, as shown in Fig.2 [47]. Also, as reported by Guimera and his colleagues [33], some random networks without well defined community structures may have quite high modularity values due to fluctuations.…”

Section: Optimization Based Algorithmsmentioning

confidence: 73%

“…Fig. 2 The local search process of the GA algorithm [47]. In (a), GA is applied to the karate network.…”

Section: Optimization Based Algorithmsmentioning

confidence: 99%

Discovering Communities from Social Networks: Methodologies and Applications

Yang

Liu²,

Liu³

2010

Handbook of Social Network Technologies and Applications

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“…At present, most of the clustering algorithms are based on statistical theory. For example, the clustering method based on VSM use the vector of Euclidean distance or cosine distance to calculate the relationship between texts [7], the basic idea is according to such as word frequency statistics information to get the feature term weights, and formatted vectors [8]. This approach ignores the semantic correlation between words and words, documents and documents thus reduced the clustering accuracy [9].…”

Section: Introductionmentioning

confidence: 99%

Improved K-Means Algorithm in Text Semantic Clustering

Ma¹

2014

TOCSJ

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Abstract:Text clustering is a very important technology in the area of text data mining. The semantic calculation method can greatly improve the computational. The aim of this paper is to improve the existing text clustering algorithms, for Chinese text and used semantic clustering method. First, in similarity calculation module of the clustering, used a staged and integrated semantic similarity algorithm, the text semantic and context factors was blended in the each computation stage. Then improved the traditional K-means algorithm, which used the priority strategy to divide up the relative centralize data at first, reduced the randomness of initial center, ensured that each cluster partition of the sample points have high similarity. Finally, the experiments have proved that the proposed algorithm not only can improve the accuracy of clustering, but also has the very high stability.

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Complex Network Clustering Algorithms

Cited by 60 publications

References 33 publications

ABC and IFC: Modules Detection Method for PPI Network

ABC and IFC: Modules Detection Method for PPI Network

Discovering Communities from Social Networks: Methodologies and Applications

Improved K-Means Algorithm in Text Semantic Clustering

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