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Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

“…Firstly, an architecture for the clustering ensemble based on incremental genetic-based algorithms is proposed consisting of two phases: (i) to produce cluster partitions as initial populations, (ii) to combine cluster partitions and to generate final clustering solution by incremental genetic based clustering ensemble learning algorithm. …”
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MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar

“…In this paper, we propose a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

“…The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. …”
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Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Noraziah, Ahmad

“…This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. …”
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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

“…Although the swmcan algorithm solves the noise problem in multi-view data, its initial and final graphs are independent and cannot learn from each other. …”
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Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling by Ullah, Wasif, Mohd Fadzil Faisae, Ab Rashid, Muhammad Ammar, Nik Mu’tasim

“…Unlike previous studies that focus on isolated cost factors, this research formulated an integrated mathematical model for CHF holistically capturing labor, energy consumption, maintenance, and late penalty costs. The GTLBO algorithm incorporates a unique hybrid initialization strategy, generating 10 % of the initial population using a Greedy algorithm to enhance exploration efficiency. …”
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Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi

“…In this paper has been proposed a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. …”
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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

“…Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. …”
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An initial state of design and development of intelligent knowledge discovery system for stock exchange database by Che Mat @ Mohd Shukor, Zamzarina, Khokhar, Rashid Hafeez, Md Sap, Mohd Noor

“…We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
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Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation by Lateh, Masitah bdul

“…The proposed algorithm named as Box-Whisker Data Transformation considered all samples contain in a MLCC dataset in order to generate artificial samples. …”
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Data Analysis and Machine Learning Algorithms Evaluation for Bioliq AI-based Predictive Tool by Samuel Simbine, Augusto

“…The mass production hasn’t initiated due to the inefficiency of the process and the dependency on experts. …”
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Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

“…The input weights and hidden layer biases are randomly generated. In this paper, the particle swan optimization algorithm (PSO) was used to obtain an optimal set of initial parameters for Reduce Kernel FLN (RK-FLN), thus, creating an optimal RKFLN classifier named PSO-RKELM. …”
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Attacks detection in 6G wireless networks using machine learning by Saeed, Mamoon M., Saeed, Rashid A., Gaid, Abdulguddoos S. A., Mokhtar, Rania A., Khalifa, Othman Omran, Ahmed, Zeinab E.

“…The Attacks Detection in 6G (AD6Gs) wireless networks created by this research uses a Machine Learning (ML) algorithm. The pre-processing stage of the ML-AD6Gs process is the initial step. …”
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Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization by Muhammad Akmal, Remli, Nor Syahidatul Nadiah, Ismail, Noor Azida, Sahabudin, Nor Bakiah, Abd Warif

“…In this work, several choices of initialization methods are compared and experimental results indicated that the algorithm is sensitive to the initial value of kinetic parameters. …”
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Dual optimization approach in discrete Hopfield neural network by Guo, Yueling, Zamri, Nur Ezlin, Mohd Kasihmuddin, Mohd Shareduwan, Alway, Alyaa, Mansor, Mohd. Asyraf, Li, Jia, Zhang, Qianhong

“…Therefore, this research contributes to the improvement of the learning and retrieval phases by integrating the Hybrid Differential Evolution Algorithm and Swarm Mutation respectively. …”
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An enhanced opposition-based firefly algorithm for solving complex optimization problems by Ling, Ai Wong, Hussain Shareef, Azah Mohamed, Ahmad Asrul Ibrahim

“…This study introduces some methods to enhance the performance of original fi refl y algorithm. The proposed enhanced opposition fi refl y algorithm (EOFA) utilizes opposition-based learning in population initialization and generation jumping while the idea of inertia weight is incorporated in the updating of fi refl y’s position. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling by Alfred, Rayner, Loo, Yew Jie, Obit, Joe Henry, Lim, Yuto, Haviluddin, Haviluddin, Azman, Azreen

“…Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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An ensemble data summarization approach based on feature transformation to learning relational data by Chung, Seng Kheau

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The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

“…IG-ANMI algorithm improves G-ANMI by developing a new attribute-oriented initialization method in which part of initial chromosomes is generated by using the attributes partitions. …”
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