A hybrid clustering algorithm based on improved GWO and KHM clustering

2021 ◽  
pp. 1-14
Author(s):  
Feng Xue ◽  
Yongbo Liu ◽  
Xiaochen Ma ◽  
Bharat Pathak ◽  
Peng Liang
Keyword(s):  
Clustering Algorithm ◽  
Test Functions ◽  
Convergence Factor ◽  
Step Size ◽  
Local Optima ◽  
Hybrid Clustering ◽  
Clustering Problem ◽  
Dynamic Weight ◽  
The Stability ◽  
Harmonic Means

To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.

scholarly journals ANALYSIS OF THE APPLICABILITY CRITERION FOR K MEANS CLUSTERING ALGORITHM RUN TEN NUMBER OF TIMES ON THE FIRST 25 NUMBERS OF THE FIBONACCI SERIES

2021 ◽  
pp. 206-213
Author(s):  
Guntuboyina Divya ◽  
R.Satya Ravindra Babu
Keyword(s):  
Objective Function ◽  
Unsupervised Learning ◽  
Key Words ◽  
Clustering Algorithm ◽  
Fibonacci Series ◽  
Local Optima ◽  
Clustering Problem ◽  
Cluster Evaluation ◽  
Random Initialization ◽  
Numeric Data

In this research investigation Analysis Of The Applicability Criterion For K Means Clustering Algorithm Run Ten Number Of Times On The First 25 Numbers Of The Fibonacci Series is performed. For this analysis RCB Model Of Applicability Criterion For K Means Clustering Algorithm is used. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. K- Means clustering algorithm is a scheme for clustering continuous and numeric data. As K-Means algorithm consists of scheme of random initialization of centroids, every time it is run, it gives different or slightly different results because it may reach some local optima. Quantification of such aforementioned variation is of some importance as this sheds light on the nature of the Discrete K-Means Objective function with regards its maxima and minima. The K-Means Clustering algorithm aims at minimizing the aforementioned Objective function. The RCB Model Of Applicability Criterion for K-Means Clustering aims at telling us if we can use the K-Means Clustering Algorithm on a given set of data within acceptable variation limits of the results of the K-Means Clustering Algorithm when it is run several times. KEY WORDS: K-means clustering algorithm, RCB model and Cluster evaluation.

BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER

2020 ◽  
Vol 20 (06) ◽  
pp. 2050032
Author(s):  
CHONG ZHANG ◽  
XUANJING SHEN ◽  
HAIPENG CHEN
Keyword(s):  
Brain Tumor ◽  
Clustering Algorithm ◽  
Guided Filter ◽  
Gaussian Kernel ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Hybrid Clustering ◽  
Simple Linear Iterative Clustering ◽  
The Stability ◽  
Sensitivity Specificity

Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-based fuzzy C-means (K[Formula: see text]GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall.

scholarly journals Clustering by Hybrid K-Harmonic Means and Biogeography Based Optimization Algorithm for Medical Data

2020 ◽  
Vol 9 (6) ◽  
pp. 517-522
Keyword(s):  
Clustering Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Medical Data ◽  
Local Optima ◽  
Computation Cost ◽  
Actual Creation ◽  
Partition Clustering ◽  
Harmonic Means

: In today trendy world hybrid based optimized data clustering is unique and imperative clustering tool in the area of data mining, which is dynamic research of actual creation problems. The oldest and furthermost commonly used popular clustering technique is the K-means(KM) algorithm, which is very complex and for the initialization of the cluster centroid and it will easily go for premature converge. This initialization problem of K-means can be evaded by built in boost function of K-Harmonic Means, which is centroid based clustering algorithm and also unresponsive for collection of initial partition clustering , but it can easily go for pre-matured conjunction in local optima. To avoid this convergence problem, this proposed algorithm uses Boosting K-harmonic means(KHM) algorithm with BBO to produce more precise, robust, better clustering solution in few number of iterations, evade conning in local optima and simply convergence to relate with Harmonic Means, BBO algorithms. Biogeography based algorithm works with the concept of emigration and immigration of inhabitants from one location to another location, Which has high computation cost. For avoiding this high computation cost in this hybrid optimization technique Biogeography-Based Optimization (BBO) is integrated with K-Harmonic means algorithm to produce optimum and effective clustering solution with faster convergence. BBO is universal optimization methods to solve utmost of the optimization problem, which is an production based generation of evolutionary algorithm (EA)that augments a function by stochastically and re peatedly improving the clustering solution of quality, or fitness function. The experimental results of this paper shown as the projected method is very resourceful and faster to afford better clustering solution in less number of repetitions for medical data

scholarly journals Hybrid Clustering of Long and Short-read for Improved Metagenome Assembly

2021 ◽  
Author(s):  
Yakang Lu ◽  
Lizhen Shi ◽  
Marc W. Van Goethem ◽  
Volkan Sevim ◽  
Michael Mascagni ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Gene Clusters ◽  
Good Alternative ◽  
Label Propagation ◽  
Short Read ◽  
Hybrid Clustering ◽  
Clustering Problem ◽  
Long Reads ◽  
Metagenome Assembly ◽  
Or Gene

ABSTRACTNext-generation sequencing has enabled metagenomics, the study of the genomes of microorganisms sampled directly from the environment without cultivation. We previously developed a proof-of-concept, scalable metagenome clustering algorithm based on Apache Spark to cluster sequence reads according to their species of origin. To overcome its under-clustering problem on short-read sequences, in this study we developed a new, two-step Label Propagation Algorithm (LPA) that first forms clusters of long reads and then recruits short reads to these clusters. Compared to alternative label propagation strategies, this hybrid clustering algorithm (hybrid-LPA) yields significantly larger read clusters without compromising cluster purity. We show that adding an extra clustering step before assembly leads to improved metagenome assemblies, predicting more complete genomes or gene clusters from a synthetic metagenome dataset and a real-world metagenome dataset, respectively. These results suggest that hybrid-LPA is a good alternative to current metagenome assembly practice by providing benefits in both scalability and accuracy on large metagenome datasets.Availability and implementationhttps://bitbucket.org/zhong_wang/hybridlpa/src/master/[email protected]

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

scholarly journals The BV solution of the parabolic equation with degeneracy on the boundary

2016 ◽  
Vol 14 (1) ◽  
pp. 272-282
Author(s):  
Huashui Zhan ◽  
Shuping Chen
Keyword(s):  
Boundary Condition ◽  
Parabolic Equation ◽  
Well Posedness ◽  
Test Functions ◽  
The Stability

AbstractConsider a parabolic equation which is degenerate on the boundary. By the degeneracy, to assure the well-posedness of the solutions, only a partial boundary condition is generally necessary. When 1 ≤ α < p – 1, the existence of the local BV solution is proved. By choosing some kinds of test functions, the stability of the solutions based on a partial boundary condition is established.

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

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