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About 8,407 papers for “clustering algorithm selection”

Cluster-Head Selection Using Fuzzy Clustering Algorithm

J.Rathi/AP*1 & S.Gowri/AP2 · Zenodo (CERN European Organization for Nuclear Research) · 2017

A Sensor is a device that responds and detects some type of input from both the physical or environmental conditions, such as pressure, heat, light, etc. The output of the sensor is generally an electrical signal that is transmitted to a controller for further processing. Wireless Sensor Networks (WSN) is composed of a collection of devices that transfer data through signals with each other through a wireless medium. A WSN comprise of a huge quantity of clusters of sensor nodes that can inter communicate with all nodes and with the base station by means of wireless link. All clusters are corresponding to a cluster head to provide a direct communication to the base station and in addition with other cluster heads. Cluster head selection turns out to be a considerable problem because of its dynamic circumstance. In this paper, proposed a novel technique to enhance the LEACH protocol by using Fuzzy Possibilistic Clustering Algorithm (FPCM). The experimental observation reveals that the FPCM based cluster- head selection algorithm performs better than the cluster head election approach used in LEACH and can enable the network lifetime longer.

Cited by 0Open access

A dynamic clonal selection immune clustering algorithm

Yaowen Chen, Lin Huang, Weiming Luo et al. · 2008

According to the basis of clonal selection immune algorithm and hierarchical clustering, a dynamic clonal selection immune clustering algorithm is presented, which no pre-knowledge is needed. The proposed algorithm bases on antibody affinity, to recognize antigen, restrain and merge antibody. By using aiNET immune network model, the algorithm mutates location of antibodies, in which the mutating rate is dynamically adjusted with inverse proportion to the number of immune evolution generations. After dynamic mutation, the similar antibodies are merged again, and the same processes repeats until it meets the ending condition. Experimental results showed that the proposed algorithm is more coincidental reality of clustering and more preferable performance than traditional ones.

Cited by 2

Clustering-Based Dynamic Materialized View Selection Algorithm

An Gong, Weijing Zhao · 2008

With the base of proposing materialized view similarity function, the paper proposes clustering-based dynamic materialized view selection algorithm. It firstly clusters materialized views, and then dynamically adjusts materialized view set. So, it eliminates the "jitter", which dynamic materialized view selection algorithm generally has. The experimental results show that the algorithm not only improves the overall query response performance, but also reduces the computational cost which will bespent during updating materialized view.

Cited by 20

Satellite selection algorithm based on hierarchical clustering

Yuanhua Liu, Yani Hou, Xinliang Niu · 2023

In the process of navigation and positioning, one of the factors affecting the positioning accuracy is the spatial geometric distribution of the satellite. It is very important to select the satellite with a good spatial distribution. For the traditional satellite selection algorithm based on azimuth Angle and pitch Angle, Because in the satellite selection process, the first lowest satellite is forced to add a fixed angle as a criterion for satellite selection,the satellite selection in the area with dense satellite distribution is more concentrated, which is not conducive to positioning. On the basis of calculating the azimuth and elevation, In this paper, a satellite selection algorithm based on hierarchical clustering is proposed.

Cited by 0

A Fuzzy Clustering Algorithm for SNP Selection

ZHANG Bo, ZHOU Conghua, ZHANG Fuquan, ZHANG Ting, JIANG Yueming · SHILAP Revista de lepidopterología · 2019

In the selection of Single Nucleotide Polymorphism(SNP) from high-dimensional genetic data with few samples,in order to make the selected SNP subset highly represent all SNP information and achieve data dimension reduction,an improved method is proposed on the basis of Fuzzy C-Mean(FCM) algorithm,which is named GN-FCM.By introducing the weight factor of SNP,the difference of importance degree of SNP site is quantified.Meanwhile,the neighborhood regular term of key SNP is introduced into the loss function of fuzzy clustering,so as to mine the correlation between highly important SNP and other SNPs in the neighborhood.Experimental results show that GN-FCM has better convergence.Compared with DW-FCM algorithm,the accuracy of the constructed SNP subsets by this algorithm in Support Vector Machine(SVM),Decision Tree(DT) and Naïve Bayesian(NB) classification is improved by 5.73%,3.40% and 3.79% respectively,and the F1 value is improved by 4.01%,3.20% and 2.22% respectively.

Cited by 0Open access

Cluster-Head Selection Using Fuzzy Clustering Algorithm

J.Rathi/AP*1 & S.Gowri/AP2 · Zenodo (CERN European Organization for Nuclear Research) · 2017

A Sensor is a device that responds and detects some type of input from both the physical or environmental conditions, such as pressure, heat, light, etc. The output of the sensor is generally an electrical signal that is transmitted to a controller for further processing. Wireless Sensor Networks (WSN) is composed of a collection of devices that transfer data through signals with each other through a wireless medium. A WSN comprise of a huge quantity of clusters of sensor nodes that can inter communicate with all nodes and with the base station by means of wireless link. All clusters are corresponding to a cluster head to provide a direct communication to the base station and in addition with other cluster heads. Cluster head selection turns out to be a considerable problem because of its dynamic circumstance. In this paper, proposed a novel technique to enhance the LEACH protocol by using Fuzzy Possibilistic Clustering Algorithm (FPCM). The experimental observation reveals that the FPCM based cluster- head selection algorithm performs better than the cluster head election approach used in LEACH and can enable the network lifetime longer.

Cited by 1Open access

A Fuzzy Clustering Algorithm for Portfolio Selection

Flávio Gabriel Duarte, Leandro Nunes de Castro · 2019

This work proposes the use of a Fuzzy Clustering Algorithm for asset allocation based on their correlation. The objective of the algorithm is to propose the allocation to help investors improve their investment process, suggesting the allocation using the information of the groups and the membership degree of each asset to each group. This work is different from the approaches already proposed in the literature, which essentially use hierarchical clustering algorithms, whereas in this proposal we use a fuzzy partitioning method. The membership degree of each asset to the group was used to determine the percentage of asset allocation: the closer to the medoid, the greater its allocation. Experiments were carried out using data from the Brazilian Stock Exchange and the assets eligible to enter into the allocation were those that were part of the Ibovespa index at the time of portfolio rebalancing. The results were compared with other allocation methods and with the Ibovespa index itself. The proposed algorithm illustrates the potential of soft-computing and machine learning techniques in portfolio optimization.

Cited by 6

Model selection for each clustering algorithm considered.

Luan Carlos de Sena Monteiro Ozelim (16914117), Dimas Betioli Ribeiro (16914120), José Antonio Schiavon (16914123) et al. · Figshare · 2023

<p>For color blindness accessibility, the legend’s first(second) row describes the first(second) column of the grid.</p>

Cited by 0Open access

An Efficient Active Constraint Selection Algorithm for Clustering

Viet-Vu Vu, Nicolas Labroche, Bernadette Bouchon‐Meunier · 2010

In this paper, we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of queries and their associated must-link and can-not link constraints to help constraints based clustering algorithms to converge. Some works on active constraints learning have already been proposed but they are only applied to K-Means like clustering algorithms which are known to be limited to spherical clusters while we are interested in constraints-based clustering algorithms that deals with clusters of arbitrary shapes and sizes (like Constrained-DBSCAN, Constrained-Hierarchical Clustering. . . ). Our novel approach relies on a k-nearest neighbors graph to estimate the dense regions of the data space and generates queries at the frontier between clusters where the cluster membership is most uncertain. Experiments show that our framework improves the performance of constraints based clustering algorithms.

Cited by 14

Immune Clustering Clonal Selection Algorithm for TSP Solution

Yong Zhao · 2010

In this paper,an immune clonal clustering selection algorithm for solving TSP problems was proposed.Firstly,Utilizing the k_means clustering method to Divided TSPinto Some partition dynamically,afterwards,The Immune clonal selection algorithm is going onExact optimization for the TSP within subgroup.According to neighborhood K Focus connection to get connected to the global problem of large-scale TSP and get satisfactory solution.Experimental results show that the proposed algorithm is effective and feasible.

Cited by 0

Negative Selection Algorithm Based on Antigen Density Clustering

Chao Yang, Lin Jia, Bing-Qiu Chen et al. · IEEE Access · 2020

The negative selection algorithm (NSA) is one of the basic algorithms of the artificial immune system. In the traditional negative selection algorithm, candidate detectors are randomly generated without considering the uneven distributions of self-antigens and nonself-antigens, thereby resulting in many redundant detectors, and it is difficult for these detectors to fully cover the area of nonself-antigens. To overcome the problem of low detector generation efficiency, a negative selection algorithm that is based on antigen density clustering (ADC-NSA) is proposed in this paper. The algorithm divides the process of detector generation into three steps: the first step is to calculate the density of the antigens by using the method of antigen density clustering to select nonself-clusters. The second step is to prioritize the abnormal points (nonself-antigens that are not clustered) as the centers of candidate detectors and to generate the detectors via calculation. The third step is to generate the detectors via the traditional algorithm. Detector generation via these three steps can reduce the randomness of the detector generation in the traditional algorithm, thereby improving the efficiency of detector generation. The experimental results demonstrate that on the BCW and KDD-Cup datasets, the negative selection algorithm that is based on antigen density clustering can effectively increase the detection rate while reducing the false-positive rate compared with the traditional negative selection algorithm (RNSA) and two improved algorithms at the same expected coverage.

Cited by 29Open access

HMM Model Selection in a File Clustering Algorithm

Dennis Geels · 2007

We employed cross-validation to select the number of hidden states and the frequency of parameter estimation for a Hidden Markov Model used to cluster les in OceanStore. 1

Cited by 0

harmony search algorithm for clustering with feature selection

Carlos Cobos, Elizabeth León, Martha Mendoza · Revista Facultad de Ingeniería Universidad de Antioquia · 2013

This paper presents a new clustering algorithm, called IHSK, with feature selection in a linear order of complexity. The algorithm is based on the combination of the harmony search and K-means algorithms. Feature selection uses both the concept of variability and a heuristic method that penalizes the presence of dimensions with a low probability of contributing to the current solution. The algorithm was tested with sets of synthetic and real data, obtaining promising results.

Cited by 16Open access

Feature Selection Algorithm Based on K-means Clustering

Xue Tang, Min Dong, Sheng Bi et al. · 2017

In order to improve the performance of the feature selection algorithm, a feature selection algorithm based on K-means clustering is designed. The algorithm makes use of the idea of K-means clustering based on cosine distance to cluster the features, so that the obtained feature subset has strong correlation and no redundancy. The experimental results show that the feature selection algorithm based on K-means clustering has high efficiency for classification tasks and has short running time, so the algorithm has strong practicability for feature selection.

Cited by 6