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Research on K-Value Selection Method of K-Means Clustering Algorithm
ET Journal, 2019 Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the ...
Chunhui Yuan, Haitao Yang
semanticscholar +1 more source
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce [PDF]
, 2014 The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures.
Elgohary, Ahmed +3 more
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Zhishi guanli luntan, 2020 [Purpose/significance] From the perspective of industry chain, this paper takes virtual reality technology as an example, constructs VR patent industry chain corpus, and explores the technical theme, research and development hotspot and future ...
Chen Ling, Lin Ping, Duan Yaoqing
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CLUSTERING KABUPATEN BERDASARKAN LUAS HUTAN MENGGUNAKAN METODE K-MEANS DI PROVINSI JAWA TENGAH
Jurnal Teknologi Industri Pertanian, 2023 Indonesia is one of the countries with the largest forest in the world. The tropical climate and high rainfall cause a lot of biodiversity in Indonesia’s forests. The existence of these forests can be utilized by many parties, both the government and the
Yusri Eli Hotman Turnip , Rina Fitriana
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Privacy-Preserving and Outsourced Multi-User k-Means Clustering [PDF]
, 2014 Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or organizations with limited computing and storage resources.
Bertino, Elisa +4 more
core +3 more sources
, 2018 The recent framework of compressive statistical learning aims at designing tractable learning algorithms that use only a heavily compressed representation-or sketch-of massive datasets.
Jacques, Laurent, Schellekens, Vincent
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Dynamic load balancing in parallel KD-tree k-means [PDF]
, 2010 One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions.
Di Fatta, Giuseppe, Pettinger, David
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Penerapan Algoritma K-Means Untuk Mengklasifikasi Data Obat
Jurnal Sisfokom, 2023 Pengklasifikasian data obat pada sebuah instansi yang bergerak pada bidang Kesehatan merupakan hal yang sangat penting. Kegiatan tersebut tidak lepas dari pengawasan serta monitoring setiap harinya karena pengolahan data obat termasuk inti dalam ...
Ferdy Pangestu Ferdy Pangestu +3 more
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Journal of Statistical Computation and Simulation, 2017 Traditionally, practitioners initialize the {\tt k-means} algorithm with centers chosen uniformly at random. Randomized initialization with uneven weights ({\tt k-means++}) has recently been used to improve the performance over this strategy in cost and run-time.
Yoder, Jordan, Priebe, Carey E.
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Faster K-Means Cluster Estimation
, 2017 There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for ...
A Likas, DT Pham, SP Lloyd, T Kanungo
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