A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique

Zhou, Bing; Bei, Lu; Saeidlou, Salman

doi:10.1080/01969722.2022.2110682

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…Classification is used to predict the number of discrete values. Meanwhile, clustering is a method for partitioning datasets into several groups called clusters (Zhou & Saeidlou, 2024).…”

Section: Machine Learningmentioning

confidence: 99%

Bitcoin Price Prediction Model Development Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)

Cahyadi,

Zahra

2024

JLMA

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Cryptocurrency is a virtual currency that can be used as a financial or economic standard, foreign currency reserve, and as a means of payment in some countries. The value that goes up and down every time is not easy to predict using logic. This is a problem for investors, besides that investors lack knowledge about the direction of crypto money movement. In addition, there is no system that can predict the price of Bitcoin, so this can cause investors to take the wrong steps in transactions and can cause losses. To avoid this risk, a system is needed that can predict bitcoin prices using data mining techniques, namely forecasting, the algorithms used are CNN and LSTM. The data used is Bitcoin closing price data from January 1, 2017, to April 26, 2023. The data is divided into 80% training data and 20% testing data. The prediction results are evaluated using MAPE which gets a MAPE value of 0.037 or 3.7% in the CNN algorithm, while the LSTM algorithm gets a value of 0.065 or 6.5%. The MAPE results of the two algorithms are in the MAPE range <10%, so it can be said that the ability of the forecasting model is very good so that it can be used as a reference to determine the prediction of bitcoin prices in the next few periods.

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“…Classification is used to predict the number of discrete values. Meanwhile, clustering is a method for partitioning datasets into several groups called clusters (Zhou & Saeidlou, 2024).…”

Section: Machine Learningmentioning

confidence: 99%

Bitcoin Price Prediction Model Development Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)

Cahyadi,

Zahra

2024

JLMA

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“…In hybrid clustering, the aggregation function creates a new grouping similar to all first clusters [48]. Table 11 shows previous research related to hybrid methodologies.…”

Section: Determining Convergence Clustermentioning

confidence: 99%

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

Faisal,

Rahman,

Mulyadi

et al. 2024

Decis. Mak. Appl. Manag. Eng.

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Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy C-Means (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues.

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“…They then proposed a graph partitioning algorithm to find the consensus function. Meta-clustering is another approach have also been explored [32,33,34,35]. In this method, they treated each base clustering solution as an entity.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Clustering Approach for Modeling Hidden Categorization Perspectives for Cloud Workloads

Daraghmeh¹,

Agarwal²,

Jararweh

2023

Preprint

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Effectively managing cloud resources is a complex task due to the interdependencies of various cloud-hosted services and applications. This task is integral to workload categorization, which groups similar cloud workloads to inform workload scheduling and resource management procedures. Although traditional clustering algorithms can categorize workloads into single data grouping patterns, the multifaceted nature of cloud workloads may conceal multiple valid categorization perspectives, indicating a need for a more adaptable clustering approach. This paper presents a novel ensemble clustering approach to enhance the scheduling workload categorization process in cloud computing. Our approach combines various normalization and transformation techniques, including principal component analysis, to form multiple data preprocessing pipelines. The data derived from these pipelines then serve as input for multiple base clustering learners. A novel combined score based on the Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index is then employed to select the optimal models and preprocessing setups. The clustering outcomes of these models are encoded and inputted into a meta-clustering algorithm, effectively capturing complex categorization perspectives. Evaluation of this approach using real-world workload trace data from Microsoft Azure significantly enhances workload segmentation efficacy, thereby improving resource management and quality of service in cloud data centers. This method offers a promising pathway toward improved workload clustering in cloud computing, demonstrating the practical utility of advanced ensemble clustering techniques.

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A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique

Cited by 10 publications

References 42 publications

Bitcoin Price Prediction Model Development Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)

Bitcoin Price Prediction Model Development Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)

A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

An Ensemble Clustering Approach for Modeling Hidden Categorization Perspectives for Cloud Workloads

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