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automatic Data clustering Based Mean Best Artificial Bee Colony Algorithm

Computers, Materials & Continua 2021年第8期68卷 1575-1593页

作者： Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Computer Department Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Center for Artificial Intelligence Technology(CAIT) Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Computer Sciences Universiti Sains Malaysia11800PenangMalaysia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmmanJordan

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine learning.The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres.Artificial Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of randomness.However,ABC has some weaknesses,such as balancing exploration and exploitation.To improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is used.Furthermore,to solve the main issues of FCM,automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC.The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

关键词： Artificial bee colony automatic clustering natural images validity index number of clusters

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Continuous Attribute Reduction Method Based on an automatic clustering Algorithm and Decision Entropy

Continuous Attribute Reduction Method Based on an Automatic ...

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第36届中国控制会议

作者： Hairong Sun Rui Wang Bixia Xie Yao Tian School of Control and Computer Engineering North China Electric Power University Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation

ISBN: (纸本)9781538629185

The data in the process of power plant are mostly continuous.Aimed at the problems existing in the discretization of the continuous attributes with associated relationship,a strategy combining k-means clustering algorithm and the degree of compatibility of the decision table was proposed.The degree of compatibility was taken as judgment conditions to determine whether a new cluster type is added,while ensuring that the degree of compatibility can be accepted.This method take full account of the relationship among variables.The automatic clustering is realized when discretizing the continuous attributes,which makes the clustering results effective and reasonable.An attribute reduction algorithm based on approximate decision entropy is used to extract the main relations of the system,and the dimensionality of the data set can be effectively reduced.The improved scheme is applied to continuous data of main steam temperature system of thermal power plant,and the validity of the method is verified.

关键词： automatic clustering Continuous attribute Attribute reduction Decision Entropy Degree of compatibility

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Fault diagnosis for down-hole conditions of sucker rod pumping systems based on the FBH-SC method

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Petroleum Science 2015年第1期12卷 135-147页

作者： Kun Li Xian-Wen Gao Hai-Bo Zhou Ying Han College of Engineering Bohai University College of Information Science and Engineering Northeastern University

Dynamometer cards are commonly used to analyze down-hole working conditions of pumping systems in actual oil production. Nowadays, the traditional supervised learning methods heavily rely on the classification accuracy of the training samples. In order to reduce the errors of manual classification, an automatic clustering algorithm is proposed and applied to diagnose down-hole conditions of pumping systems. The spectral clustering （SC） is a new clustering algorithm, which is suitable for any data distribution. However, it is sensitive to initial cluster centers and scale parameters, and needs to predefine the cluster number. In order to overcome these shortcom- ings, we propose an automatic clustering algorithm, fast black hole-spectral clustering （FBH-SC）. The FBH algo- rithm is used to replace the K-mean method in SC, and a CritC index function is used as the target function to automatically choose the best scale parameter and clus- tering number in the clustering process. Different simulation experiments were designed to define the relationship among scale parameter, clustering number, CritC index value, and clustering accuracy. Finally, an example is given to validate the effectiveness of the proposed algorithm.

关键词： Sucker rod pumping systems Fault diagnosis Spectral clustering automatic clustering Fast black hole algorithm

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对于聚合聚类终止条件的研究

对于聚合聚类终止条件的研究

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第十八届全国数据库学术会议

作者：钱海蕾钱卫宁周傲英复旦大学计算机科学与工程系

1引言聚类过程就是把数据集合分成若干类(簇,cluster),使得每个簇中的数据尽量相似,而属于不同簇的数据尽量不同。聚类分析是数据挖掘中一项非常重要的技术,在空间数据挖掘、信息检索等领域有着广泛的应用。聚类可以被看作是一个无监督... 详细信息

1引言聚类过程就是把数据集合分成若干类(簇,cluster),使得每个簇中的数据尽量相似,而属于不同簇的数据尽量不同。聚类分析是数据挖掘中一项非常重要的技术,在空间数据挖掘、信息检索等领域有着广泛的应用。聚类可以被看作是一个无监督的学习过程。现有的一

关键词： Data mining automatic clustering Cluster tree

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