Search Results - (( data distribution clustering algorithm ) OR ( parameter estimation strategy algorithm ))

Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach by Marpaung, Faridawaty, Ramadhani, Fanny, Dinata, Dewan

“…The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. …”
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Scheduled activity energy-aware distributed cluster- based routing algorithm for wireless sensor networks with non-uniform node distribution by Nokhanji, Nooshin

“…Therefore, in this study, a new algorithm called Scheduled-Activity Energy Aware Distributed Clustering (SA-EADC) is proposed. …”
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Fuzzy Soft Set Clustering for Categorical Data by Iwan Tri Riyadi, Yanto, Ani, Apriani, Rofiul, Wahyudi, Cheah, Wai Shiang, Suprihatin, in, Rahmat, Hidayat

“…This research provides categorical data with fuzzy clustering technique due to soft set theory and multinomial distribution. …”
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Statistical performance of agglomerative hierarchical clustering technique via pairing of correlation-based distances and linkage methods by Nurshaziana, Mohamad Shamsuri

“…Five tables of summary for choosing appropriate clustering algorithms according to data distribution were produced. …”
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A scheduled activity energy aware distributed clustering algorithm for wireless sensor networks with nonuniform node distribution by Nokhanji, Nooshin, Mohd Hanapi, Zurina, Subramaniam, Shamala, Mohamed, Mohamad Afendee

“…Energy aware distributed clustering (EADC) is one of the cluster-based routing protocols proposed for networks with nonuniform node distribution, which can effectively balance the energy consumption among the nodes. …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”

Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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A cluster-based hybrid replica control protocol for high availability in data grid by Mabni, Zulaile

“…In Data Grid, data replication is a widely used technique for managing data, where exact copies of data or replicas are created and stored at many distributed sites. …”
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An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets by Azlin, Ahmad, Rubiyah, Yusof, Nor Saradatul Akmar, Zulkifli, Mohd Najib, Ismail

“…The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. …”
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An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets by Ahmad, Azlin, Yusof, Rubiyah, Zulkifli, Nor Saradatul Akma, Ismail, Mohd Najib

“…The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. …”
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An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis by Tie, K. H., A., Senawi, Chuan, Z. L.

“…The k-means and the Gaussian mixture distribution were adopted as the clustering algorithms and each algorithm was tested on four datasets with four distinct clustering evaluation criteria: Calinski-Harabasz, Davies-Bouldin, Gap and Silhouette. …”
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Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi by Dejamkhooy, Abdolmajid, Asefi, Sajjad

“…Since these parameters are affected by different operation conditions and load changings, then an accurate estimation strategy is quite necessary. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem by Haneen Abdul Wahab, Abdul Raheem

“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization by Hussain Mohammad Yousef Abu Dalbouh

Optimizing wireless sensor networks: A survey of clustering strategies and algorithms by Hamim, Sakib Iqram, Azamuddin, Ab Rahman

“…Clustering helps in the distribution of energy evenly in the network minimizing the number of unnecessary transmissions. …”
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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning by Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping

“…Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. …”
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Development of an effective clustering algorithm for older fallers by Goh, Choon Hian, Wong, Kam Kang, Tan, Maw Pin *, Ng, Siew Cheok, Chuah, Yea Dat, Kwan, Ban Hoe

“…The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. …”
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Development Of Fall Risk Clustering Algorithm In Older People by Wong, Kam Kang

“…The proposed algorithm consists of several stages, includes data pre-processing, feature selection, feature extraction, clustering and characteristic interpretation. …”
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