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Li H, Li H, Chen X, Wei K. An Improved Pigeon-Inspired Optimization for Clustering Analysis Problems. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2017. [DOI: 10.1142/s1469026817500146] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Clustering is an important technology in data mining, which attempts to partition a set of objects into clusters based on the values of their attributes. [Formula: see text]-means is a simple and efficient data clustering algorithm. However, it highly depends on the initial solution and is extremely easy to be trapped in local optima. In contrast, meta-heuristic algorithms show good performance to break through the local optima obstacle. In this paper, we propose an improved pigeon-inspired optimization (IPIO) algorithm towards resolving this problem. The algorithm uses an object-based initialization method to generate the initial population and introduces a parametric control strategy to navigate the flying direction. Meanwhile, the climb process of monkey algorithm (MA) with dimension by dimension improvement is adopted to strengthen the local search ability. In this paper, experiments over six real datasets are conducted to validate the effectiveness of IPIO. The experimental results show that IPIO is an efficient alternative in resolving the clustering analysis problem.
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Affiliation(s)
- Haiyun Li
- School of Electronic Information and Electrical Engineering, Tianshui Normal University Tianshui, Gansu, China
| | - Haifeng Li
- School of Software, Dalian University of Technology, Dalian, Liaoning, China
| | - Xin Chen
- School of Software, Dalian University of Technology, Dalian, Liaoning, China
- Guangxi High School Key Laboratory of Complex System and Computational Intelligence Nanning, Guangxi, China
| | - Kaibin Wei
- School of Electronic Information and Electrical Engineering, Tianshui Normal University Tianshui, Gansu, China
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Abstract
This chapter proposed different hybrid clustering methods based on combining particle swarm optimization (PSO), gravitational search algorithm (GSA) and free parameters central force optimization (CFO) with each other and with the k-means algorithm. The proposed methods were applied on 5 real datasets from the university of California, Irvine (UCI) machine learning repository. Comparative analysis was done in terms of three measures; the sum of intra cluster distances, the running time and the distances between the clusters centroids. The initial population for the used algorithms were enhanced to minimize the sum of intra cluster distances. Experimental results show that, increasing the number of iterations doesn't have a noticeable impact on the sum of intra cluster distances while it has a negative impact on the running time. K-means combined with GSA (KM-GSA), PSO combined with GSA (PSO-GSA) gave the best performance according to the sum of intra cluster distances while K-means combined with PSO (KM-PSO) and KM-GSA were the best in terms of the running time. Finally, KM-GSA and GSA have the best performance.
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55
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Kumar V, Chhabra JK, Kumar D. Grey Wolf Algorithm-Based Clustering Technique. JOURNAL OF INTELLIGENT SYSTEMS 2017. [DOI: 10.1515/jisys-2014-0137] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractThe main problem of classical clustering technique is that it is easily trapped in the local optima. An attempt has been made to solve this problem by proposing the grey wolf algorithm (GWA)-based clustering technique, called GWA clustering (GWAC), through this paper. The search capability of GWA is used to search the optimal cluster centers in the given feature space. The agent representation is used to encode the centers of clusters. The proposed GWAC technique is tested on both artificial and real-life data sets and compared to six well-known metaheuristic-based clustering techniques. The computational results are encouraging and demonstrate that GWAC provides better values in terms of precision, recall, G-measure, and intracluster distances. GWAC is further applied for gene expression data set and its performance is compared to other techniques. Experimental results reveal the efficiency of the GWAC over other techniques.
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Affiliation(s)
- Vijay Kumar
- 1Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India
| | - Jitender Kumar Chhabra
- 2Computer Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India
| | - Dinesh Kumar
- 3Computer Science and Engineering Department, GJUS and T, Hisar, Haryana, India
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56
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Pakrashi A, Chaudhuri BB. A Kalman filtering induced heuristic optimization based partitional data clustering. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.07.057] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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57
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An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.04.026] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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58
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Ghafarzadeh H, Bouyer A. An Efficient Hybrid Clustering Method Using an Artificial Bee Colony Algorithm and Mantegna Lévy Distribution. INT J ARTIF INTELL T 2016. [DOI: 10.1142/s0218213015500347] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Data clustering is a common data mining techniques used in many applications such as data analysis and pattern recognition. K-means algorithm is the common clustering method which has fallen into the trap of local optimization and does not always create the optimized response to the problem, although having more advantages such as high speed. Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. However, there are still some problems in ABC algorithm, like inability to find the best solution from all possible solutions. Due to the large step of searching equation in ABC, the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence ability of the ABC, Mantegna Lévy distribution random walk is proposed and incorporated with ABC. The new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm. The proposed algorithm benefits from Mantegna Lévy distribution to promote the ABC algorithm in solving the number of functional evaluation and also obtaining better convergence speed and high accuracy in a short time. We empirically evaluate the performance of our proposed method on nine standard datasets taken from the UCI Machine Learning Repository. The experimental results show that the proposed algorithm has ability to obtain better results in terms of convergence speed, accuracy, and reducing the number of functional evaluation.
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Affiliation(s)
- Habib Ghafarzadeh
- Faculty of Computer Engineering, Miandoab Branch, Islamic Azad University, Miandoab, Iran
| | - Asgarali Bouyer
- Faculty of Computer and Information Technology, Azarbaijan Shahid Madani University, Tabriz, Iran
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60
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Amiri E, Mahmoudi S. Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.12.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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61
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Gallagher M. Towards improved benchmarking of black-box optimization algorithms using clustering problems. Soft comput 2016. [DOI: 10.1007/s00500-016-2094-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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62
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Wang R, Zhou Y, Qiao S, Huang K. Flower Pollination Algorithm with Bee Pollinator for cluster analysis. INFORM PROCESS LETT 2016. [DOI: 10.1016/j.ipl.2015.08.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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63
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Peng H, Wang J, Shi P, Riscos-Núñez A, Pérez-Jiménez MJ. An automatic clustering algorithm inspired by membrane computing. Pattern Recognit Lett 2015. [DOI: 10.1016/j.patrec.2015.08.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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64
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Convalescing Cluster Configuration Using a Superlative Framework. ScientificWorldJournal 2015; 2015:180749. [PMID: 26543895 PMCID: PMC4620246 DOI: 10.1155/2015/180749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Revised: 09/17/2015] [Accepted: 09/21/2015] [Indexed: 11/18/2022] Open
Abstract
Competent data mining methods are vital to discover knowledge from databases which are built as a result of enormous growth of data. Various techniques of data mining are applied to obtain knowledge from these databases. Data clustering is one such descriptive data mining technique which guides in partitioning data objects into disjoint segments. K-means algorithm is a versatile algorithm among the various approaches used in data clustering. The algorithm and its diverse adaptation methods suffer certain problems in their performance. To overcome these issues a superlative algorithm has been proposed in this paper to perform data clustering. The specific feature of the proposed algorithm is discretizing the dataset, thereby improving the accuracy of clustering, and also adopting the binary search initialization method to generate cluster centroids. The generated centroids are fed as input to K-means approach which iteratively segments the data objects into respective clusters. The clustered results are measured for accuracy and validity. Experiments conducted by testing the approach on datasets from the UC Irvine Machine Learning Repository evidently show that the accuracy and validity measure is higher than the other two approaches, namely, simple K-means and Binary Search method. Thus, the proposed approach proves that discretization process will improve the efficacy of descriptive data mining tasks.
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Kumar Y, Sahoo G. A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm. AI COMMUN 2015. [DOI: 10.3233/aic-150677] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yugal Kumar
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. E-mails: ,
| | - G. Sahoo
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. E-mails: ,
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68
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Accelerated Simplified Swarm Optimization with Exploitation Search Scheme for Data Clustering. PLoS One 2015; 10:e0137246. [PMID: 26348483 PMCID: PMC4562660 DOI: 10.1371/journal.pone.0137246] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 08/14/2015] [Indexed: 11/19/2022] Open
Abstract
Data clustering is commonly employed in many disciplines. The aim of clustering is to partition a set of data into clusters, in which objects within the same cluster are similar and dissimilar to other objects that belong to different clusters. Over the past decade, the evolutionary algorithm has been commonly used to solve clustering problems. This study presents a novel algorithm based on simplified swarm optimization, an emerging population-based stochastic optimization approach with the advantages of simplicity, efficiency, and flexibility. This approach combines variable vibrating search (VVS) and rapid centralized strategy (RCS) in dealing with clustering problem. VVS is an exploitation search scheme that can refine the quality of solutions by searching the extreme points nearby the global best position. RCS is developed to accelerate the convergence rate of the algorithm by using the arithmetic average. To empirically evaluate the performance of the proposed algorithm, experiments are examined using 12 benchmark datasets, and corresponding results are compared with recent works. Results of statistical analysis indicate that the proposed algorithm is competitive in terms of the quality of solutions.
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69
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An Efficient Optimization Method for Solving Unsupervised Data Classification Problems. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:802754. [PMID: 26336509 PMCID: PMC4532808 DOI: 10.1155/2015/802754] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 06/11/2015] [Accepted: 06/29/2015] [Indexed: 11/29/2022]
Abstract
Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
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71
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Forsati R, Keikha A, Shamsfard M. An improved bee colony optimization algorithm with an application to document clustering. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.02.048] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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72
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Kumar Y, Sahoo G. Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy. Soft comput 2015. [DOI: 10.1007/s00500-015-1719-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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73
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Ye ZW, Wang MW, Liu W, Chen SB. Fuzzy entropy based optimal thresholding using bat algorithm. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.02.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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74
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Ji J, Pang W, Zheng Y, Wang Z, Ma Z. A novel artificial bee colony based clustering algorithm for categorical data. PLoS One 2015; 10:e0127125. [PMID: 25993469 PMCID: PMC4439097 DOI: 10.1371/journal.pone.0127125] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 04/11/2015] [Indexed: 11/19/2022] Open
Abstract
Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.
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Affiliation(s)
- Jinchao Ji
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
- Key Lab of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Wei Pang
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Yanlin Zheng
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
- Key Lab of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China
| | - Zhe Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Zhiqiang Ma
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, China
- Key Lab of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China
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A novel clustering algorithm inspired by membrane computing. ScientificWorldJournal 2015; 2015:929471. [PMID: 25874264 PMCID: PMC4385684 DOI: 10.1155/2015/929471] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 09/07/2014] [Indexed: 11/17/2022] Open
Abstract
P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.
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78
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79
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İnkaya T, Kayalıgil S, Özdemirel NE. Ant Colony Optimization based clustering methodology. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.11.060] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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80
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81
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Abstract
As an important boundary-based clustering algorithm, support vector clustering (SVC) can benefit many real applications owing to its capability of handling arbitrary cluster shapes, especially those directly or indirectly related to pattern exploration and description. As the application deepens, the importance of performance (i.e. criterions of accuracy and efficiency) of SVC increases. To identify gaps in the current methods and propose novel research directions for SVC, we present a survey of the literature in this area. Our approach is to classify the most recent advances into either theory or application. For theoretical contributions, advances related to parameter selection and optimization, dual-problem solutions, and cluster labeling are introduced. We also simultaneously summarize the advantages and drawbacks of each study. With respect to applications, we clearly describe eight groups of schemes based on SVC, either as individual or hybrid methods. Finally, we identify the gaps in SVC research and suggest several future research issues and trends.
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Affiliation(s)
- Huina Li
- School of Information Engineering, Xuchang University, Xuchang 461000, P. R. China
| | - Yuan Ping
- School of Information Engineering, Xuchang University, Xuchang 461000, P. R. China
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82
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Ahmad W. Artificial Immune Optimization Algorithm. IMPROVING KNOWLEDGE DISCOVERY THROUGH THE INTEGRATION OF DATA MINING TECHNIQUES 2015. [DOI: 10.4018/978-1-4666-8513-0.ch006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Artificial immune system (AIS) is a paradigm inspired by processes and metaphors of natural immune system (NIS). There is a rapidly growing interest in AIS approaches to machine learning and especially in the domain of optimization. Of particular interest is the way human body responds to diseases and pathogens as well as adapts to remain immune for long periods after a disease has been combated. In this chapter, we are presenting a novel multilayered natural immune system (NIS) inspired algorithms in the domain of optimization. The proposed algorithm uses natural immune system components such as B-cells, Memory cells and Antibodies; and processes such as negative clonal selection and affinity maturation to find multiple local optimum points. Another benefit this algorithm presents is the presence of immunological memory that is in the form of specific memory cells which keep track of previously explored solutions. The algorithm is evaluated on two well-known numeric functions to demonstrate the applicability.
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83
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A Hybrid Clustering Algorithm Based on Fuzzy c-Means and Improved Particle Swarm Optimization. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014. [DOI: 10.1007/s13369-014-1424-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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84
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Elkamel A, Gzara M, Ben-Abdallah H. A bio-inspired hierarchical clustering algorithm with backtracking strategy. APPL INTELL 2014. [DOI: 10.1007/s10489-014-0573-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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85
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86
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Ant algorithm for modifying an inconsistent pairwise weighting matrix in an analytic hierarchy process. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1630-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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87
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A hybrid monkey search algorithm for clustering analysis. ScientificWorldJournal 2014; 2014:938239. [PMID: 24772039 PMCID: PMC3967398 DOI: 10.1155/2014/938239] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Accepted: 01/22/2014] [Indexed: 11/21/2022] Open
Abstract
Clustering is a popular data analysis and data mining technique. The k-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 the k-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.
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88
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Kumar Y, Sahoo G. A charged system search approach for data clustering. PROGRESS IN ARTIFICIAL INTELLIGENCE 2014. [DOI: 10.1007/s13748-014-0049-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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89
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A New Algorithm for Data Clustering Based on Cuckoo Search Optimization. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2014. [DOI: 10.1007/978-3-319-01796-9_6] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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90
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Armano G, Farmani MR. Clustering Analysis with Combination of Artificial Bee Colony Algorithm and k-Means Technique. ACTA ACUST UNITED AC 2014. [DOI: 10.7763/ijcte.2014.v6.852] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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91
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92
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93
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Huang CL, Huang WC, Chang HY, Yeh YC, Tsai CY. Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.05.003] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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94
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Banharnsakun A, Sirinaovakul B, Achalakul T. The best-so-far ABC with multiple patrilines for clustering problems. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.02.047] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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95
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Tsai CW, Tseng SP, Yang CS, Chiang MC. PREACO: A fast ant colony optimization for codebook generation. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.01.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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96
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97
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Yan X, Zhu Y, Zou W, Wang L. A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.04.025] [Citation(s) in RCA: 108] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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98
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Su ZG, Wang PH, Shen J, Li YG, Zhang YF, Hu EJ. Automatic fuzzy partitioning approach using Variable string length Artificial Bee Colony (VABC) algorithm. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.06.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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99
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Hatamlou A. In search of optimal centroids on data clustering using a binary search algorithm. Pattern Recognit Lett 2012. [DOI: 10.1016/j.patrec.2012.06.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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100
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dos Santos DS, Bazzan AL. Distributed clustering for group formation and task allocation in multiagent systems: A swarm intelligence approach. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.03.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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