Search Results - (( java implication based algorithm ) OR ( feature reduction clustering algorithm ))

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 higher the TERR threshold value is set, the more the feature subset size will be, regardless of the type of clustering algorithm and the clustering evaluation criterion are used. …”
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Feature Selection And Enhanced Krill Herd Algorithm For Text Document Clustering by Abualigah, Laith Mohammad Qasim

“…In this study, a new method for solving the TD clustering problem worked in the following two stages: (i) A new feature selection method using particle swarm optimization algorithm with a novel weighting scheme and a detailed dimension reduction technique are proposed to obtain a new subset of more informative features with low-dimensional space.…”
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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction by Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.

“…The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. …”

Optimised content-social based features for fake news detection in social media using text clustering approach by Yahya, Adnan Hussein Ali

“…In addition, this thesis tackles the feature selection problem by designing a novel wrapper feature selection method based on the Hybrid Flower Pollination Algorithm (HFPA). …”
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An evolutionary-based term reduction approach to bilingual clustering of Malay-English corpora by Rayner Alfred, Leow, Ching Leong, Joe Henry Obit

“…Hence, this encourages the study of term reduction technique in clustering bilingual documents. …”
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Pairwise clusters optimization and cluster most significant feature methods for anomaly-based network intrusion detection system (POC2MSF) / Gervais Hatungimana by Hatungimana, Gervais

“…The speed, memory and accuracy of IDS are affected by inappropriate features reduction method or ignorance of irrelevant features. …”
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Efficient classifying and indexing for large iris database based on enhanced clustering method by Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy, Khalaf, Ahmad Taha

“…The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques by ASHRAF, AMNA, MOHD NAWI, NAZRI, MUHAMMAD AAMIR, MUHAMMAD AAMIR

“…In the first phase, the manifold learning approach is used to improve the ‘feature selection by clustering’. Clustering algorithms such as K-means, spectral clustering, and the Gaussian Mixer Model have been tested with manifold learning approaches for adaptive feature selection. …”
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Customer mobile behavioral segmentation and analysis in telecom using machine learning by Sharaf Addin, Eman Hussein, Admodisastro, Novia Indriaty, Mohd Ashri, Siti Nur Syahirah, Kamaruddin, Azrina, Chew, Yew Chong

“…The dashboard visualizes the cluster analysis based on demographics, behavior, and region features. …”
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Prediction of Alzheimer disease using improved MMSE ensemble regressor based on magnetic resonance images by Farzan, Ali

“…So, a rank based feature selection algorithm is proposed to address these issues. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

“…The second segmentation algorithm combines Delaunay triangulation clustering in the spatial domain and Particle Swarm Optimization (PSO). …”
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Self-Organized Wireless Sensor Network (SOWSN) for dense jungle applications by Hakim, Galang Persada Nurani, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Alghaihab, Abdullah, Yusoff, Siti Hajar, Adesta, Erry Yulian Triblas

“…To develop the traits needed for such SOWSN nodes, three types of computational intelligence mechanisms have been featured in the design. The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. …”
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A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

“…A vector quantization model that incorporate rough sets attribute reduction and rules generation with a modified version of the K-means clustering algorithm was developed, implemented and tested as a part of a speech recognition framework, in which the Learning Vector Quantization (LVQ) neural network model was used in the pattern matching stage. …”
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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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Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques by Hassan Zaidi, Syed Muhammad, Jamil Alsayaydeh, Jamil Abedalrahim, Khan, Abdul Hafeez, Khan, Abdullah Ayub, AlZubi, Ahmad Ali, Ogunshola, Benny, Herawan, Safarudin Gazali

“…The k-means clustering algorithm is then applied to group visually similar chart images. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

“…In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush