Results 11 to 20 of about 17,769,086 (225)
Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation [PDF]
2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), 2016 (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Xhafa Xhafa, Fatos +3 more
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$k$-means clustering of extremes [PDF]
Electronic Journal of Statistics, 2020 The $k$-means clustering algorithm and its variant, the spherical $k$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $k$-means algorithm can be applied in the analysis of only the extremal observations from a data set.
Janßen, Anja, Wan, Phyllis
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How to Use K-means for Big Data Clustering? [PDF]
Pattern Recognition, 2022 K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data.
R. Mussabayev +3 more
semanticscholar +1 more source
K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province
Desimal, 2022 The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district.
Ummu Wachidatul Latifah +2 more
doaj +1 more source
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
Electronics, 2020 The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random ...
Mohiuddin Ahmed +2 more
semanticscholar +1 more source
K-Means and Alternative Clustering Methods in Modern Power Systems
IEEE Access, 2023 As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases.
S. Miraftabzadeh +3 more
semanticscholar +1 more source
Algorithms, 2020 Two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means‖ type of an initialization strategy. The second proposal also uses multiple lower-dimensional subspaces
Joonas Hämäläinen +2 more
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Proceedings of the VLDB Endowment, 2012 Over half a century old and showing no signs of aging, k -means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k -means is crucial for obtaining a good final solution.
Bahmani, Bahman +4 more
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Unsupervised Multi-View K-Means Clustering Algorithm
IEEE Access, 2023 Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
semanticscholar +1 more source
The development of a GIS for New Deal Archaeology
Bulletin of the History of Archaeology, 2011 I have recently launched an effort to create a GIS of all New Deal-funded archaeological investigations conducted in the 48 states that comprised the USA during the Great Depression (Means 2011).
Bernard K. Means
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