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Optimized Cartesian K-Means [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2015 Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two
Jianfeng Wang +5 more
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K-means** - a fast and efficient K-means algorithms
International Journal of Intelligent Information and Database Systems, 2018 K-means often converges to a local optimum. In improved versions of K-means, k-means++ is well-known for achieving a rather optimum solution with its cluster initialisation strategy and high computational efficiency. Incremental K-means is recognised for its converging to the empirically global optimum but having a high complexity due to its stepping ...
Cuong Duc Nguyen, Trong Hai Duong
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Penggunaan Metode K-Means dan K-Means++ Sebagai Clustering Data Covid-19 di Pulau Jawa
Teknika, 2022 Virus Corona (Covid-19) merupakan penyakit menular yang dapat ditularkan antara hewan dan manusia. Pada akhir Desember 2019, virus itu teridentifikasi di Provinsi Wuhan, Cina.
Nursatio Nugroho, Faisal Dharma Adhinata
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CoRR, 2020 This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid distance computation. The Ball k-means can accurately find the neighbor clusters for each cluster resulting distance computations only between a point and its neighbor ...
Shuyin Xia +6 more
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The comparative study of text documents clustering algorithms
Environment Conservation Journal, 2015 Clustering is one of the most significant research area in the field of data mining and considered as an important tool in the fast developing information explosion era.Clustering systems are used more and more often in text mining, especially in ...
Mohammad Eiman Jamnezhad, Reza Fattahi
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Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 PT. Perkebunan Nusantara 1 is engaged in oil palm production with a total land area of 1,144 Ha. The formulation of this research can determine productive land clusters based on land area, number of trees, number of stages, and palm oil production ...
Mutammimul Ula +3 more
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CoRR, 2022 The $k$-means is one of the most important unsupervised learning techniques in statistics and computer science. The goal is to partition a data set into many clusters, such that observations within clusters are the most homogeneous and observations between clusters are the most heterogeneous.
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Using classification and K-means methods to predict breast cancer recurrence in gene expression data
Journal of Medical Signals and Sensors, 2022 Background: Breast cancer is a type of cancer that starts in the breast tissue and affects about 10% of women at different stages of their lives.
Mohammadreza Sehhati +3 more
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Advances in Neural Information Processing Systems 36, 2023 The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation. To obtain higher quality solutions, Lattanzi and Sohler (ICML 2019) proposed augmenting $k$-means++ with $O(k \log \log
Lorenzo Beretta 0001 +3 more
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CoRR, 2015 The developed algorithm is similar with "Christopher F. Barnes, A new multiple path search technique for residual vector quantizers, 1994", but we conduct the research independently and apply it in data/feature compression and image ...
Jianfeng Wang +5 more
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