Search Results - Feature selection weighted k-means clustering algorithm

Optimization configuration of flexible interconnection device DG based on improved E-C-Kmeans and GA under SOP support by Yang, Zhichun, Yang, Lei, He, Junxiong, Yang, Fan, Min, Huaidong, Zhang, Nan, Su, Jing

“…•The E-C-Kmeans algorithm introduces entropy-based feature weighting and density-aware mechanisms, improving clustering robustness for volatile wind–solar data…”
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An optimized SVM-RFE based feature selection and weighted entropy K-means approach for big data clustering in mapreduce by Madan, Suman, C, Komalavalli, Bhatia, Manjot Kaur, Laroiya, Chetna, Arora, Monika

“… Hence, this research introduced the Competitive Jaya Leader Harris Hawks Optimization assisted Entropy Weighted Power K-Means Clustering (CJayaLHHO_EWPKMC…”
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Automatic image annotation and retrieval using weighted feature selection by Wang, Lei, Khan, Latifur

“… In this paper, we propose weighted feature selection algorithm as a solution…”
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Weighted Multiview K-Means Clustering with L2 Regularization by Hussain, Ishtiaq, Nataliani, Yessica, Ali, Mehboob, Hussain, Atif, Mujlid, Hana M., Almaliki, Faris A., Rahimi, Nouf M.

“… This study introduces two novel weighted multiview k-means algorithms, W-MV-KM and weighted multiview k-means using L2 regularization, W-MV-KM-L2, designed specifically for clustering multiview data…”
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Self-Weighted Euler k-Means Clustering by Xin, Haonan, Lu, Yihang, Tang, Haoliang, Wang, Rong, Nie, Feiping

“…-formula>-means, kernel <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-means uses a kernel trick to map the raw data into a feature space to better describe the data with improved clustering performance…”
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Bi-Partitioned Feature-Weighted K-Means Clustering for Detecting Insurance Fraud Claim Patterns by Combert, Francis Kwaku, Xie, Shengkun, Lawniczak, Anna T.

“…The weighted K-means clustering algorithm is widely recognized for its ability to assign varying importance to features in clustering tasks…”
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A practical data-driven sampling framework combining KMKFC clustering and MCFS feature selection for large-scale building energy efficiency analysis by Zheng, Run, Lei, Lei

“… Initially, an Entropy-Weighted K-means (EWKM) algorithm coupled with the Davies-Bouldin Index (DBI…”
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Semi-Supervised K-Means DDoS Detection Method Using Hybrid Feature Selection Algorithm by Gu, Yonghao, Li, Kaiyue, Guo, Zhenyang, Wang, Yongfei

“… rate and high false positive rate. In order to tackle these issues, this paper presents a semi-supervised weighted k-means detection method…”
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Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering by Cordeiro de Amorim, Renato, Mirkin, Boris

“…This paper represents another step in overcoming a drawback of K-Means, its lack of defense against noisy features, using feature weights in the criterion…”
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Self-Weighted Multi-View k-means Algorithm by LIN Hechuan, XU Huiying, ZHU Xinzhong, HUANG Xiao, LIU Ziyang

“… To address this issue, this paper proposes a new multi-view clustering framework, namely the Self-weighted Multi-view k-Means (SwMKM) algorithm…”
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A Globally Collaborative Multi-View k-Means Clustering by Sinaga, Kristina P., Yang, Miin-Shen

“… Existing feature-weighted MV k-means (MVKM) algorithms often neglect effective dimensionality reduction such that their scalability and interpretability are limited…”
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Debiasing weighted multi-view k-means clustering based on causal regularization by Huang, Xiuqi, Tao, Hong, Ni, Haotian, Hou, Chenping

“… This paper proposes a causal regularized debiased multi-view k-means clustering (DMKC) method to counteract redundant feature correlations stemming from sample selection bias…”
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A Perceptually Important Points Approach Based on Imputation Clustering with Weighted Distance Techniques for Big Data Reduction in Internet of Things Cloud by Edje, Efetobor Abel, Shaffie, Abd Latiff Muhammad, Howe, Chan Weng

“… These techniques mainly remove redundant data records by similarity comparison between the features of a given sensed dataset without considering the data records or points…”
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A Combination of Particle Swarm Optimization and Minkowski Weighted K-Means Clustering: Application in Lateralization of Temporal Lobe Epilepsy by Jamali-Dinan, Samira-Sadat, Soltanian-Zadeh, Hamid, Bowyer, Susan M., Almohri, Haidar, Dehghani, Hamed, Elisevich, Kost, Nazem-Zadeh, Mohammad-Reza

“… Minkowski weighted K-Means (MWK-Means) addresses the issue of sensitivity to noisy features, but is sensitive to the initialization of clusters, and so the algorithm may similarly converge to local optima…”
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Time series k-means: A new k-means type smooth subspace clustering for time series data by Huang, Xiaohui, Ye, Yunming, Xiong, Liyan, Lau, Raymond Y.K., Jiang, Nan, Wang, Shaokai

“… In this paper, we propose a new k-means type smooth subspace clustering algorithm named Time Series k-means (TSkmeans…”
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Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm by Chakraborty, Saptarshi, Das, Swagatam

“…We propose a simple variable (feature) weight learning strategy for the Gaussian means algorithm which can automatically determine the number of clusters in a dataset as well…”
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Bi-level gene selection of cancer by combining clustering and sparse learning by Chen, Junnan, Wen, Bo

“… We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied…”
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Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach by Chakraborty, Saptarshi, Das, Swagatam

“…In context to high-dimensional clustering, the concept of feature weighting has gained considerable importance over the years to capture the relative degrees of importance of different features…”
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Classification of bioinformatics workflows using weighted versions of partitioning and hierarchical clustering algorithms by Lord, Etienne, Diallo, Abdoulaye Baniré, Makarenkov, Vladimir

“… Clustering algorithms can be applied either for regrouping similar workflows for their simultaneous execution on a server, or for dispatching some lengthy workflows to different servers…”
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Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams by Lee, Geon Ju, Jung, Jason J., Camacho, David

“… The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep…”
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