Automatic clustering using genetic algorithms

Liu, Yongguo; Wu, Xindong; Shen, Yi-Dong

doi:10.1016/j.amc.2011.06.007

Cited by 105 publications

(85 citation statements)

References 31 publications

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“…The noise method guiding the heuristic search produces to explore the solution space has led to the proposal of the recent combinatorial optimization metaheuristics technique [38], which has been applied to K-means clustering [22], as well as other application fields of the noising method such as task allocation [39], and the clique partitioning problem [40]. In addition, the noise method considers the optimal results as the outcome of a series of fluctuating data converging towards the genuine ones, and the features and the variants of the noise method are detailed, the tunings of their parameters when are applied to different combinatorial optimization problems have been summarized in [36].…”

Section: Initial Population Using Noise and K-means++ Methodsmentioning

confidence: 99%

“…In early GAs, 0, 1 binary digits are usually used, and it has been shown that more natural representations can obtain a more efficient and better optimal solution. However, with the increase of the length of the 0, 1 strings, more CPU computing time is needed, which causes the genetic performance to decline [20,22,36]. Therefore, OD representation is utilized to describe the chromosome in this paper; furthermore, a noise method is proposed to use the chromosomes initial selection where K-means++ is also used to select the initial seeds (see Section 2.3).…”

Section: Gps Data Descriptionmentioning

confidence: 99%

“…This is a novel gene rearrangement technique, which can handle chromosomes of unequal lengths, but it only focuses on gene rearrangement operations without considering between the structure chromosomes, resulting in difficulty in determining the reference chromosome and target chromosome. Therefore, we first obtain the best chromosome in descending order according to its fitness values, and a pair of chromosomes are chosen using an existing roulette wheel technique (RWT) [22,28] where the chromosomes CR i , CR j are picked with the probability…”

Section: Crossover Operationmentioning

confidence: 99%

See 2 more Smart Citations

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

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Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA) with density and noise for K-means clustering (NoiseClust). In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome) are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city's situations in four taxi GPS data sets.

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Section: Initial Population Using Noise and K-means++ Methodsmentioning

confidence: 99%

Section: Gps Data Descriptionmentioning

confidence: 99%

Section: Crossover Operationmentioning

confidence: 99%

See 1 more Smart Citation

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

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“…The task of clustering is to divide an unlabeled model into several subsets according to some criteria, and the similar samples are classified into the same class. Many existing clustering methods such as rough set clustering [15], fuzzy clustering [16,17] and support vector clustering [18] have been used in many fields, including data analysis, fault diagnosis, text classification, pattern recognition, image processing, radar target detection, biological engineering, space remote sensing technology, etc.…”

Section: Clustering Algorithmmentioning

confidence: 99%

Research on the method of information system risk state estimation based on clustering particle filter

Cui¹,

Hong²,

Jiang³

et al. 2017

Open Physics

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With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.

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“…Network security management is an important part of the protection of personal privacy and account security. It is the future development of network application that is one of the important topics [1][2][3][4][5]. Network security management mainly includes intrusion detection and trust management, that is achieved using artificial intelligence, the clustering theory and evidence theory to carry out the management of the network security, which is a clonal selection theory of clustering based on fuzzy clustering algorithm; the clonal selection method of anomaly detection based on fuzzy clustering algorithm for Intrusion Detection based on evidence theory, which proposes compensation method of the evidence combination rule and reliability of the average compensation based on the sharing of evidence combination rule based trust management; and trust management model for P2P based on the improved evidence combination rule [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Application of Clonal Selection Clustering Algorithm for Anomaly Detection in Network Security Management

Zhang¹,

Wang²,

Li³

2015

TOAUTOCJ

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Network security being of practical significance, the importance of network application, the network anomaly detection and the generalization ability are studied as a key link in the network security management. The key technology of network security management is based on: artificial intelligence theory as the research object; the clonal selection method of anomaly detection based on fuzzy clustering algorithm, to solve the anomaly detection of low efficiency; high false alarm rate, proposes the compensation method of evidence combination rule based on the rule of sharing, to solve the problem of the information fusion of evidence combination rule of conflict and defects; and P2P trust management model based on the improved evidence combination rule to solve the P2P system. It is however, difficult to effectively deal with the malicious node attack, besides it can't effectively deal with the uncertain information and other issues.

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Automatic clustering using genetic algorithms

Cited by 105 publications

References 31 publications

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Research on the method of information system risk state estimation based on clustering particle filter

Application of Clonal Selection Clustering Algorithm for Anomaly Detection in Network Security Management

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