scholarly journals A Hybrid Monkey Search Algorithm for Clustering Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xin Chen ◽  
Yongquan Zhou ◽  
Qifang Luo
Keyword(s):  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Real Life ◽  
Local Optimum ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Bee Colony ◽  
Optimum Solution ◽  
Monkey Search

Clustering is a popular data analysis and data mining technique. Thek-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of thek-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

scholarly journals An Improved Animal Migration Optimization Algorithm for Clustering Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Mingzhi Ma ◽  
Qifang Luo ◽  
Yongquan Zhou ◽  
Xin Chen ◽  
Liangliang Li
Keyword(s):  
Clustering Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Local Optimum ◽  
Data Sets ◽  
Data Mining Technique ◽  
Clustering Problem ◽  
Real Life Data ◽  
Animal Migration Optimization ◽  
Animal Migration

Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm migration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields. The well-known method in solving clustering problems isk-means clustering algorithm; however, it highly depends on the initial solution and is easy to fall into local optimum. To improve the defects of thek-means method, this paper used IAMO for the clustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better performance than that of thek-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem.

Global Artificial Bee Colony Search Algorithm for Data Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 48-59
Author(s):  
Zeeshan Danish ◽  
Habib Shah ◽  
Nasser Tairan ◽  
Rozaida Gazali ◽  
Akhtar Badshah
Keyword(s):  
Data Mining ◽  
Vector Quantization ◽  
Data Clustering ◽  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Computation Time ◽  
Search Space ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Bee Colony

Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.

scholarly journals A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2790
Author(s):  
Qi Xiong ◽  
Xinman Zhang ◽  
Shaobo He ◽  
Jun Shen
Keyword(s):  
Fractional Order ◽  
Iris Recognition ◽  
Search Algorithm ◽  
Fitness Function ◽  
Histogram Equalization ◽  
Enhancement Effect ◽  
Local Optimum ◽  
Long Distance ◽  
Iris Image ◽  
Bee Colony

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

scholarly journals Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2250
Author(s):  
Mei Li ◽  
Gai-Ge Wang ◽  
Helong Yu
Keyword(s):  
Artificial Bee Colony ◽  
Flow Shop ◽  
Real Life ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony ◽  
Mutation Strategy

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

scholarly journals PARSUC: A Parallel Subsampling-Based Method for Clustering Remote Sensing Big Data

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3438 ◽  
Author(s):  
Xia ◽  
Huang ◽  
Li ◽  
Zhou ◽  
Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Image Data ◽  
Data Partitioning ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Hadoop Platform ◽  
Parallel Clustering

Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD.

Sentiment Analysis on Microblogging with K-Means Clustering and Artificial Bee Colony

2019 ◽  
Vol 18 (03) ◽  
pp. 1950017 ◽  
Author(s):  
Korawit Orkphol ◽  
Wu Yang
Keyword(s):  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Global Optimum ◽  
High Dimensional ◽  
Local Optimum ◽  
Short Message ◽  
Initial State ◽  
Bee Colony ◽  
Analysis Technique ◽  
Value Decomposition

Microblogging is a type of blog used by people to express their opinions, attitudes, and feelings toward entities with a short message and this message is easily shared through the network of connected people. Knowing their sentiments would be beneficial for decision-making, planning, visualization, and so on. Grouping similar microblogging messages can convey some meaningful sentiments toward an entity. This task can be accomplished by using a simple and fast clustering algorithm, [Formula: see text]-means. As the microblogging messages are short and noisy they cause high sparseness and high-dimensional dataset. To overcome this problem, term frequency–inverse document frequency (tf–idf) technique is employed for selecting the relevant features, and singular value decomposition (SVD) technique is employed for reducing the high-dimensional dataset while still retaining the most relevant features. These two techniques adjust dataset to improve the [Formula: see text]-means efficiently. Another problem comes from [Formula: see text]-means itself. [Formula: see text]-means result relies on the initial state of centroids, the random initial state of centroids usually causes convergence to a local optimum. To find a global optimum, artificial bee colony (ABC), a novel swarm intelligence algorithm, is employed to find the best initial state of centroids. Silhouette analysis technique is also used to find optimal [Formula: see text]. After clustering into [Formula: see text] groups, each group will be scored by SentiWordNet and we analyzed the sentiment polarities of each group. Our approach shows that combining various techniques (i.e., tf–idf, SVD, and ABC) can significantly improve [Formula: see text]-means result (41% from normal [Formula: see text]-means).

scholarly journals Comparative Study of Document Clustering Algorithms

2018 ◽  
Vol 7 (4.11) ◽  
pp. 246
Author(s):  
N. M. Ariff ◽  
M. A. A. Bakar ◽  
M. I. Rahmad
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Text Clustering ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Meaningful Result ◽  
Different Types

Text clustering is a data mining technique that is becoming more important in present studies. Document clustering makes use of text clustering to divide documents according to the various topics. The choice of words in document clustering is important to ensure that the document can be classified correctly. Three different methods of clustering which are hierarchical clustering, k-means and k-medoids are used and compared in this study in order to identify the best method which produce the best result in document clustering. The three methods are applied on 60 sports articles involving four different types of sports. The k-medoids clustering produced the worst result while k-means clustering is found to be more sensitive towards general words. Therefore, the method of hierarchical clustering is deemed more stable to produce a meaningful result in document clustering analysis. 

scholarly journals Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

2021 ◽  
Vol 11 (5) ◽  
pp. 4050
Author(s):  
Waleed Alomoush ◽  
Ayat Alrosan ◽  
Ammar Almomani ◽  
Khalid Alissa ◽  
Osama A. Khashan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Medical Image Segmentation ◽  
Local Optimum ◽  
Brain Image ◽  
Bee Colony

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.

scholarly journals K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 53
Author(s):  
Qibing Jin ◽  
Nan Lin ◽  
Yuming Zhang
Keyword(s):  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Step Length ◽  
Standard Test ◽  
Local Optimum ◽  
Bee Colony ◽  
Clustering Problems ◽  
Speed And Accuracy

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

scholarly journals A hybrid artificial bee colony algorithm for numerical function optimization

2015 ◽  
Vol 26 (10) ◽  
pp. 1550109 ◽  
Author(s):  
Zakaria N. Alqattan ◽  
Rosni Abdullah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Function Optimization ◽  
Search Process ◽  
Local Optimum ◽  
Particle Movement ◽  
Numerical Function ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimization Search

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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